1
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Soper N, Yardumian I, Chen E, Yang C, Ciervo S, Oom AL, Desvignes L, Mulligan MJ, Zhang Y, Lupoli TJ. A Repurposed Drug Interferes with Nucleic Acid to Inhibit the Dual Activities of Coronavirus Nsp13. ACS Chem Biol 2024. [PMID: 38980755 DOI: 10.1021/acschembio.4c00244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
The recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlighted a critical need to discover more effective antivirals. While therapeutics for SARS-CoV-2 exist, its nonstructural protein 13 (Nsp13) remains a clinically untapped target. Nsp13 is a helicase responsible for unwinding double-stranded RNA during viral replication and is essential for propagation. Like other helicases, Nsp13 has two active sites: a nucleotide binding site that hydrolyzes nucleoside triphosphates (NTPs) and a nucleic acid binding channel that unwinds double-stranded RNA or DNA. Targeting viral helicases with small molecules, as well as the identification of ligand binding pockets, have been ongoing challenges, partly due to the flexible nature of these proteins. Here, we use a virtual screen to identify ligands of Nsp13 from a collection of clinically used drugs. We find that a known ion channel inhibitor, IOWH-032, inhibits the dual ATPase and helicase activities of SARS-CoV-2 Nsp13 at low micromolar concentrations. Kinetic and binding assays, along with computational and mutational analyses, indicate that IOWH-032 interacts with the RNA binding interface, leading to displacement of nucleic acid substrate, but not bound ATP. Evaluation of IOWH-032 with microbial helicases from other superfamilies reveals that it is selective for coronavirus Nsp13. Furthermore, it remains active against mutants representative of observed SARS-CoV-2 variants. Overall, this work provides a new inhibitor for Nsp13 and provides a rationale for a recent observation that IOWH-032 lowers SARS-CoV-2 viral loads in human cells, setting the stage for the discovery of other potent viral helicase modulators.
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Affiliation(s)
- Nathan Soper
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Isabelle Yardumian
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department of Chemistry, New York University, New York, New York 10003, United States
- Simons Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
| | - Chao Yang
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Samantha Ciervo
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Aaron L Oom
- NYU Langone Vaccine Center, Department of Medicine, New York University Grossman School of Medicine, New York, New York 10016, United States
| | - Ludovic Desvignes
- NYU Langone Vaccine Center, Department of Medicine, New York University Grossman School of Medicine, New York, New York 10016, United States
- High Containment Laboratories, Office of Science and Research, NYU Langone Health, New York, New York 10016, United States
| | - Mark J Mulligan
- NYU Langone Vaccine Center, Department of Medicine, New York University Grossman School of Medicine, New York, New York 10016, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States
- Simons Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
| | - Tania J Lupoli
- Department of Chemistry, New York University, New York, New York 10003, United States
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2
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Shu J, Wang Y, Guo W, Liu T, Cai S, Shi T, Hu W. Carbenoid-involved reactions integrated with scaffold-based screening generates a Nav1.7 inhibitor. Commun Chem 2024; 7:135. [PMID: 38866907 PMCID: PMC11169417 DOI: 10.1038/s42004-024-01213-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/30/2024] [Indexed: 06/14/2024] Open
Abstract
The discovery of selective Nav1.7 inhibitors is a promising approach for developing anti-nociceptive drugs. In this study, we present a novel oxindole-based readily accessible library (OREAL), which is characterized by readily accessibility, unique chemical space, ideal drug-like properties, and structural diversity. We used a scaffold-based approach to screen the OREAL and discovered compound C4 as a potent Nav1.7 inhibitor. The bioactivity characterization of C4 reveals that it is a selective Nav1.7 inhibitor and effectively reverses Paclitaxel-induced neuropathic pain (PINP) in rodent models. Preliminary toxicology study shows C4 is negative to hERG. The consistent results of molecular docking and molecular simulations further support the reasonability of the in-silico screening and show the insight of the binding mode of C4. Our discovery of C4 paves the way for pushing the Nav1.7-based anti-nociceptive drugs forward to the clinic.
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Affiliation(s)
- Jirong Shu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yuwei Wang
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Weijie Guo
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Tao Liu
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Song Cai
- Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Taoda Shi
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Wenhao Hu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
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3
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Carlsson J, Luttens A. Structure-based virtual screening of vast chemical space as a starting point for drug discovery. Curr Opin Struct Biol 2024; 87:102829. [PMID: 38848655 DOI: 10.1016/j.sbi.2024.102829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 06/09/2024]
Abstract
Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.
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Affiliation(s)
- Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-751 24 Uppsala, Sweden.
| | - Andreas Luttens
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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4
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Bedart C, Shimokura G, West FG, Wood TE, Batey RA, Irwin JJ, Schapira M. The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening. Sci Data 2024; 11:597. [PMID: 38844472 PMCID: PMC11156877 DOI: 10.1038/s41597-024-03443-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
Computationally screening chemical libraries to discover molecules with desired properties is a common technique used in early-stage drug discovery. Recent progress in the field now enables the efficient exploration of billions of molecules within days or hours, but this exploration remains confined within the boundaries of the accessible chemistry space. While the number of commercially available compounds grows rapidly, it remains a limited subset of all druglike small molecules that could be synthesized. Here, we present a workflow where chemical reactions typically developed in academia and unconventional in drug discovery are exploited to dramatically expand the chemistry space accessible to virtual screening. We use this process to generate a first version of the Pan-Canadian Chemical Library, a collection of nearly 150 billion diverse compounds that does not overlap with other ultra-large libraries such as Enamine REAL or SAVI and could be a resource of choice for protein targets where other libraries have failed to deliver bioactive molecules.
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Affiliation(s)
- Corentin Bedart
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
- Univ. Lille, Inserm, CHU Lille, U1286 - INFINITE - Institute for Translational Research in Inflammation, F-59000, Lille, France
| | - Grace Shimokura
- Davenport Research Laboratories, Dept. of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Frederick G West
- Department of Chemistry, University of Alberta, Edmonton, AB, T6G 2G2, Canada
| | - Tabitha E Wood
- Department of Chemistry, The University of Winnipeg, 515 Portage Avenue, Winnipeg, MB, R3B 2E9, Canada
| | - Robert A Batey
- Davenport Research Laboratories, Dept. of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Acceleration Consortium, University of Toronto, Toronto, ON, M5S 3H6, Canada
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94143, USA.
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, M5S 1A1, Canada.
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5
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Retchin M, Wang Y, Takaba K, Chodera JD. DrugGym: A testbed for the economics of autonomous drug discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596296. [PMID: 38854082 PMCID: PMC11160604 DOI: 10.1101/2024.05.28.596296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.
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Affiliation(s)
- Michael Retchin
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Yuanqing Wang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Simons Center for Computational Chemistry and Center for Data Science, New York University, New York, NY 10004
| | - Kenichiro Takaba
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation, Shizuoka 410-2321, Japan
| | - John D. Chodera
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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6
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Krishnan SR, Bung N, Srinivasan R, Roy A. Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process. J Mol Graph Model 2024; 129:108734. [PMID: 38442440 DOI: 10.1016/j.jmgm.2024.108734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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7
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Song RX, Nicklaus MC, Tarasova NI. Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries. J Comput Aided Mol Des 2024; 38:22. [PMID: 38753096 PMCID: PMC11098933 DOI: 10.1007/s10822-024-00562-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/22/2024] [Indexed: 05/19/2024]
Abstract
Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.
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Affiliation(s)
- Robert X Song
- Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD, 21702, USA
| | - Nadya I Tarasova
- Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA.
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8
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Shi T, Hu W. Asymmetric Carbene Transfer: Enhancing Chemical Diversity for Drug Discovery. Chemistry 2024:e202400971. [PMID: 38735847 DOI: 10.1002/chem.202400971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/14/2024]
Abstract
The quest to explore chemical space is vital for identifying novel disease targets, impacting both the effectiveness and safety profile of therapeutic agents. The tangible chemical space, currently estimated at a conservative 108 synthesized compounds, pales in comparison to the theoretically conceivable diversity of 1060 molecules. To bridge this vast gap, organic chemists are spearheading innovative methodologies that promise to broaden this limited chemical diversity. A beacon of this progressive wave is Asymmetric Carbene Transfer (ACT), a burgeoning strategy that significantly boosts molecular diversity with efficient bond-formation and precise chiral control. This review focuses on the capabilities of ACT in creating pharmaceutically significant molecules, encompassing an array of natural products and bioactive compounds. Through the lens of ACT, we discern its substantial influence on drug discovery, paving the way for novel therapeutic avenues by expanding the boundaries of molecular diversity. This review will shed light on prospective methodological developments of ACT and articulate their conceivable contributions to the medicinal chemistry arena.
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Affiliation(s)
- Taoda Shi
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China, 510006
- Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education", Yantai University, Yantai, 264005, China
| | - Wenhao Hu
- Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education", Yantai University, Yantai, 264005, China
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9
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Ziaikin E, Tello E, Peterson DG, Niv MY. BitterMasS: Predicting Bitterness from Mass Spectra. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:10537-10547. [PMID: 38685906 PMCID: PMC11082931 DOI: 10.1021/acs.jafc.3c09767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/02/2024]
Abstract
Bitter compounds are common in nature and among drugs. Previously, machine learning tools were developed to predict bitterness from the chemical structure. However, known structures are estimated to represent only 5-10% of the metabolome, and the rest remain unassigned or "dark". We present BitterMasS, a Random Forest classifier that was trained on 5414 experimental mass spectra of bitter and nonbitter compounds, achieving precision = 0.83 and recall = 0.90 for an internal test set. Next, the model was tested against spectra newly extracted from the literature 106 bitter and nonbitter compounds and for additional spectra measured for 26 compounds. For these external test cases, BitterMasS exhibited 67% precision and 93% recall for the first and 58% accuracy and 99% recall for the second. The spectrum-bitterness prediction strategy was more effective than the spectrum-structure-bitterness prediction strategy and covered more compounds. These encouraging results suggest that BitterMasS can be used to predict bitter compounds in the metabolome without the need for structural assignment of individual molecules. This may enable identification of bitter compounds from metabolomics analyses, for comparing potential bitterness levels obtained by different treatments of samples and for monitoring bitterness changes overtime.
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Affiliation(s)
- Evgenii Ziaikin
- Food
Science and Nutrition, The Robert H. Smith Faculty of Agriculture,
Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| | - Edisson Tello
- Department
of Food Science and Technology, College of Food, Agriculture, and
Environmental Sciences, The Ohio State University, Columbus, Ohio 43210, United States
| | - Devin G. Peterson
- Department
of Food Science and Technology, College of Food, Agriculture, and
Environmental Sciences, The Ohio State University, Columbus, Ohio 43210, United States
| | - Masha Y. Niv
- Food
Science and Nutrition, The Robert H. Smith Faculty of Agriculture,
Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
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10
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Fralish Z, Chen A, Khan S, Zhou P, Reker D. The landscape of small-molecule prodrugs. Nat Rev Drug Discov 2024; 23:365-380. [PMID: 38565913 DOI: 10.1038/s41573-024-00914-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2024] [Indexed: 04/04/2024]
Abstract
Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs encompass a wide range of different chemical structures, therapeutic indications and properties. Here we provide the first holistic analysis of the current landscape of approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug development. We highlight rationales that underlie prodrug design, their indications, mechanisms of API release, the chemistry of promoieties added to APIs to form prodrugs and the market impact of prodrugs. On the basis of this analysis, we discuss strengths and limitations of current prodrug approaches and suggest areas for future development.
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Affiliation(s)
- Zachary Fralish
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ashley Chen
- Department of Computer Science, Duke University, Durham, NC, USA
| | | | - Pei Zhou
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, USA
| | - Daniel Reker
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
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11
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Grotsch K, Sadybekov AV, Hiller S, Zaidi S, Eremin D, Le A, Liu Y, Smith EC, Illiopoulis-Tsoutsouvas C, Thomas J, Aggarwal S, Pickett JE, Reyes C, Picazo E, Roth BL, Makriyannis A, Katritch V, Fokin VV. Virtual Screening of a Chemically Diverse "Superscaffold" Library Enables Ligand Discovery for a Key GPCR Target. ACS Chem Biol 2024; 19:866-874. [PMID: 38598723 DOI: 10.1021/acschembio.3c00602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
The advent of ultra-large libraries of drug-like compounds has significantly broadened the possibilities in structure-based virtual screening, accelerating the discovery and optimization of high-quality lead chemotypes for diverse clinical targets. Compared to traditional high-throughput screening, which is constrained to libraries of approximately one million compounds, the ultra-large virtual screening approach offers substantial advantages in both cost and time efficiency. By expanding the chemical space with compounds synthesized from easily accessible and reproducible reactions and utilizing a large, diverse set of building blocks, we can enhance both the diversity and quality of the discovered lead chemotypes. In this study, we explore new chemical spaces using reactions of sulfur(VI) fluorides to create a combinatorial library consisting of several hundred million compounds. We screened this virtual library for cannabinoid type II receptor (CB2) antagonists using the high-resolution structure in conjunction with a rationally designed antagonist, AM10257. The top-predicted compounds were then synthesized and tested in vitro for CB2 binding and functional antagonism, achieving an experimentally validated hit rate of 55%. Our findings demonstrate the effectiveness of reliable reactions, such as sulfur fluoride exchange, in diversifying ultra-large chemical spaces and facilitate the discovery of new lead compounds for important biological targets.
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Affiliation(s)
- Katharina Grotsch
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Sydney Hiller
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Saheem Zaidi
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Dmitry Eremin
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Austen Le
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Yongfeng Liu
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Evan Carlton Smith
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Christos Illiopoulis-Tsoutsouvas
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Joice Thomas
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Shubhangi Aggarwal
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Julie E Pickett
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Cesar Reyes
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Elias Picazo
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Bryan L Roth
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Alexandros Makriyannis
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Vsevolod Katritch
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Valery V Fokin
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
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12
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Brocidiacono M, Francoeur P, Aggarwal R, Popov KI, Koes DR, Tropsha A. BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening. J Chem Inf Model 2024; 64:2488-2495. [PMID: 38113513 DOI: 10.1021/acs.jcim.3c01211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Deep learning methods that predict protein-ligand binding have recently been used for structure-based virtual screening. Many such models have been trained using protein-ligand complexes with known crystal structures and activities from the PDBBind data set. However, because PDBbind only includes 20K complexes, models typically fail to generalize to new targets, and model performance is on par with models trained with only ligand information. Conversely, the ChEMBL database contains a wealth of chemical activity information but includes no information about binding poses. We introduce BigBind, a data set that maps ChEMBL activity data to proteins from the CrossDocked data set. BigBind comprises 583 K ligand activities and includes 3D structures of the protein binding pockets. Additionally, we augmented the data by adding an equal number of putative inactives for each target. Using this data, we developed Banana (basic neural network for binding affinity), a neural network-based model to classify active from inactive compounds, defined by a 10 μM cutoff. Our model achieved an AUC of 0.72 on BigBind's test set, while a ligand-only model achieved an AUC of 0.59. Furthermore, Banana achieved competitive performance on the LIT-PCBA benchmark (median EF1% 1.81) while running 16,000 times faster than molecular docking with Gnina. We suggest that Banana, as well as other models trained on this data set, will significantly improve the outcomes of prospective virtual screening tasks.
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Affiliation(s)
- Michael Brocidiacono
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Paul Francoeur
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Rishal Aggarwal
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Konstantin I Popov
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - David Ryan Koes
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Alexander Tropsha
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
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Kim H, Lee K, Kim C, Lim J, Kim WY. DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening. J Chem Inf Model 2024; 64:2432-2444. [PMID: 37651152 DOI: 10.1021/acs.jcim.3c01134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address this issue, a few recent studies have attempted to use deep learning models to estimate the synthetic accessibility of many molecules rapidly. However, retrosynthetic analysis tools used to train the models rely on reaction templates automatically extracted from a large reaction database that are not domain-specific and may exhibit low chemical correctness. To overcome this limitation, we introduce DFRscore (Drug-Focused Retrosynthetic score), a deep learning-based approach for a more practical assessment of synthetic accessibility in drug discovery. The DFRscore model is trained exclusively on drug-focused reactions, providing a predicted number of minimally required synthetic steps for each compound. This approach enables practitioners to filter out compounds that do not meet their desired level of synthetic accessibility at an early stage of high-throughput virtual screening for accelerated drug discovery. The proposed strategy can be easily adapted to other domains by adjusting the synthesis planning setup of the reaction templates and starting materials.
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Affiliation(s)
- Hyeongwoo Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Kyunghoon Lee
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Chansu Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jaechang Lim
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
- AI Institute, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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14
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Zhang X, Gao H, Wang H, Chen Z, Zhang Z, Chen X, Li Y, Qi Y, Wang R. PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2024; 64:2205-2220. [PMID: 37319418 DOI: 10.1021/acs.jcim.3c00253] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.
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Affiliation(s)
- Xiangying Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Haotian Gao
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Haojie Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Zhihang Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Zhe Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Xinchong Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yifei Qi
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
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15
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Wallach I, Bernard D, Nguyen K, Ho G, Morrison A, Stecula A, Rosnik A, O’Sullivan AM, Davtyan A, Samudio B, Thomas B, Worley B, Butler B, Laggner C, Thayer D, Moharreri E, Friedland G, Truong H, van den Bedem H, Ng HL, Stafford K, Sarangapani K, Giesler K, Ngo L, Mysinger M, Ahmed M, Anthis NJ, Henriksen N, Gniewek P, Eckert S, de Oliveira S, Suterwala S, PrasadPrasad SVK, Shek S, Contreras S, Hare S, Palazzo T, O’Brien TE, Van Grack T, Williams T, Chern TR, Kenyon V, Lee AH, Cann AB, Bergman B, Anderson BM, Cox BD, Warrington JM, Sorenson JM, Goldenberg JM, Young MA, DeHaan N, Pemberton RP, Schroedl S, Abramyan TM, Gupta T, Mysore V, Presser AG, Ferrando AA, Andricopulo AD, Ghosh A, Ayachi AG, Mushtaq A, Shaqra AM, Toh AKL, Smrcka AV, Ciccia A, de Oliveira AS, Sverzhinsky A, de Sousa AM, Agoulnik AI, Kushnir A, Freiberg AN, Statsyuk AV, Gingras AR, Degterev A, Tomilov A, Vrielink A, Garaeva AA, Bryant-Friedrich A, Caflisch A, Patel AK, Rangarajan AV, Matheeussen A, Battistoni A, Caporali A, Chini A, Ilari A, Mattevi A, Foote AT, Trabocchi A, Stahl A, Herr AB, Berti A, Freywald A, Reidenbach AG, Lam A, Cuddihy AR, White A, Taglialatela A, Ojha AK, Cathcart AM, Motyl AAL, Borowska A, D’Antuono A, Hirsch AKH, Porcelli AM, Minakova A, Montanaro A, Müller A, Fiorillo A, Virtanen A, O’Donoghue AJ, Del Rio Flores A, Garmendia AE, Pineda-Lucena A, Panganiban AT, Samantha A, Chatterjee AK, Haas AL, Paparella AS, John ALS, Prince A, ElSheikh A, Apfel AM, Colomba A, O’Dea A, Diallo BN, Ribeiro BMRM, Bailey-Elkin BA, Edelman BL, Liou B, Perry B, Chua BSK, Kováts B, Englinger B, Balakrishnan B, Gong B, Agianian B, Pressly B, Salas BPM, Duggan BM, Geisbrecht BV, Dymock BW, Morten BC, Hammock BD, Mota BEF, Dickinson BC, Fraser C, Lempicki C, Novina CD, Torner C, Ballatore C, Bon C, Chapman CJ, Partch CL, Chaton CT, Huang C, Yang CY, Kahler CM, Karan C, Keller C, Dieck CL, Huimei C, Liu C, Peltier C, Mantri CK, Kemet CM, Müller CE, Weber C, Zeina CM, Muli CS, Morisseau C, Alkan C, Reglero C, Loy CA, Wilson CM, Myhr C, Arrigoni C, Paulino C, Santiago C, Luo D, Tumes DJ, Keedy DA, Lawrence DA, Chen D, Manor D, Trader DJ, Hildeman DA, Drewry DH, Dowling DJ, Hosfield DJ, Smith DM, Moreira D, Siderovski DP, Shum D, Krist DT, Riches DWH, Ferraris DM, Anderson DH, Coombe DR, Welsbie DS, Hu D, Ortiz D, Alramadhani D, Zhang D, Chaudhuri D, Slotboom DJ, Ronning DR, Lee D, Dirksen D, Shoue DA, Zochodne DW, Krishnamurthy D, Duncan D, Glubb DM, Gelardi ELM, Hsiao EC, Lynn EG, Silva EB, Aguilera E, Lenci E, Abraham ET, Lama E, Mameli E, Leung E, Christensen EM, Mason ER, Petretto E, Trakhtenberg EF, Rubin EJ, Strauss E, Thompson EW, Cione E, Lisabeth EM, Fan E, Kroon EG, Jo E, García-Cuesta EM, Glukhov E, Gavathiotis E, Yu F, Xiang F, Leng F, Wang F, Ingoglia F, van den Akker F, Borriello F, Vizeacoumar FJ, Luh F, Buckner FS, Vizeacoumar FS, Bdira FB, Svensson F, Rodriguez GM, Bognár G, Lembo G, Zhang G, Dempsey G, Eitzen G, Mayer G, Greene GL, Garcia GA, Lukacs GL, Prikler G, Parico GCG, Colotti G, De Keulenaer G, Cortopassi G, Roti G, Girolimetti G, Fiermonte G, Gasparre G, Leuzzi G, Dahal G, Michlewski G, Conn GL, Stuchbury GD, Bowman GR, Popowicz GM, Veit G, de Souza GE, Akk G, Caljon G, Alvarez G, Rucinski G, Lee G, Cildir G, Li H, Breton HE, Jafar-Nejad H, Zhou H, Moore HP, Tilford H, Yuan H, Shim H, Wulff H, Hoppe H, Chaytow H, Tam HK, Van Remmen H, Xu H, Debonsi HM, Lieberman HB, Jung H, Fan HY, Feng H, Zhou H, Kim HJ, Greig IR, Caliandro I, Corvo I, Arozarena I, Mungrue IN, Verhamme IM, Qureshi IA, Lotsaris I, Cakir I, Perry JJP, Kwiatkowski J, Boorman J, Ferreira J, Fries J, Kratz JM, Miner J, Siqueira-Neto JL, Granneman JG, Ng J, Shorter J, Voss JH, Gebauer JM, Chuah J, Mousa JJ, Maynes JT, Evans JD, Dickhout J, MacKeigan JP, Jossart JN, Zhou J, Lin J, Xu J, Wang J, Zhu J, Liao J, Xu J, Zhao J, Lin J, Lee J, Reis J, Stetefeld J, Bruning JB, Bruning JB, Coles JG, Tanner JJ, Pascal JM, So J, Pederick JL, Costoya JA, Rayman JB, Maciag JJ, Nasburg JA, Gruber JJ, Finkelstein JM, Watkins J, Rodríguez-Frade JM, Arias JAS, Lasarte JJ, Oyarzabal J, Milosavljevic J, Cools J, Lescar J, Bogomolovas J, Wang J, Kee JM, Kee JM, Liao J, Sistla JC, Abrahão JS, Sishtla K, Francisco KR, Hansen KB, Molyneaux KA, Cunningham KA, Martin KR, Gadar K, Ojo KK, Wong KS, Wentworth KL, Lai K, Lobb KA, Hopkins KM, Parang K, Machaca K, Pham K, Ghilarducci K, Sugamori KS, McManus KJ, Musta K, Faller KME, Nagamori K, Mostert KJ, Korotkov KV, Liu K, Smith KS, Sarosiek K, Rohde KH, Kim KK, Lee KH, Pusztai L, Lehtiö L, Haupt LM, Cowen LE, Byrne LJ, Su L, Wert-Lamas L, Puchades-Carrasco L, Chen L, Malkas LH, Zhuo L, Hedstrom L, Hedstrom L, Walensky LD, Antonelli L, Iommarini L, Whitesell L, Randall LM, Fathallah MD, Nagai MH, Kilkenny ML, Ben-Johny M, Lussier MP, Windisch MP, Lolicato M, Lolli ML, Vleminckx M, Caroleo MC, Macias MJ, Valli M, Barghash MM, Mellado M, Tye MA, Wilson MA, Hannink M, Ashton MR, Cerna MVC, Giorgis M, Safo MK, Maurice MS, McDowell MA, Pasquali M, Mehedi M, Serafim MSM, Soellner MB, Alteen MG, Champion MM, Skorodinsky M, O’Mara ML, Bedi M, Rizzi M, Levin M, Mowat M, Jackson MR, Paige M, Al-Yozbaki M, Giardini MA, Maksimainen MM, De Luise M, Hussain MS, Christodoulides M, Stec N, Zelinskaya N, Van Pelt N, Merrill NM, Singh N, Kootstra NA, Singh N, Gandhi NS, Chan NL, Trinh NM, Schneider NO, Matovic N, Horstmann N, Longo N, Bharambe N, Rouzbeh N, Mahmoodi N, Gumede NJ, Anastasio NC, Khalaf NB, Rabal O, Kandror O, Escaffre O, Silvennoinen O, Bishop OT, Iglesias P, Sobrado P, Chuong P, O’Connell P, Martin-Malpartida P, Mellor P, Fish PV, Moreira POL, Zhou P, Liu P, Liu P, Wu P, Agogo-Mawuli P, Jones PL, Ngoi P, Toogood P, Ip P, von Hundelshausen P, Lee PH, Rowswell-Turner RB, Balaña-Fouce R, Rocha REO, Guido RVC, Ferreira RS, Agrawal RK, Harijan RK, Ramachandran R, Verma R, Singh RK, Tiwari RK, Mazitschek R, Koppisetti RK, Dame RT, Douville RN, Austin RC, Taylor RE, Moore RG, Ebright RH, Angell RM, Yan R, Kejriwal R, Batey RA, Blelloch R, Vandenberg RJ, Hickey RJ, Kelm RJ, Lake RJ, Bradley RK, Blumenthal RM, Solano R, Gierse RM, Viola RE, McCarthy RR, Reguera RM, Uribe RV, do Monte-Neto RL, Gorgoglione R, Cullinane RT, Katyal S, Hossain S, Phadke S, Shelburne SA, Geden SE, Johannsen S, Wazir S, Legare S, Landfear SM, Radhakrishnan SK, Ammendola S, Dzhumaev S, Seo SY, Li S, Zhou S, Chu S, Chauhan S, Maruta S, Ashkar SR, Shyng SL, Conticello SG, Buroni S, Garavaglia S, White SJ, Zhu S, Tsimbalyuk S, Chadni SH, Byun SY, Park S, Xu SQ, Banerjee S, Zahler S, Espinoza S, Gustincich S, Sainas S, Celano SL, Capuzzi SJ, Waggoner SN, Poirier S, Olson SH, Marx SO, Van Doren SR, Sarilla S, Brady-Kalnay SM, Dallman S, Azeem SM, Teramoto T, Mehlman T, Swart T, Abaffy T, Akopian T, Haikarainen T, Moreda TL, Ikegami T, Teixeira TR, Jayasinghe TD, Gillingwater TH, Kampourakis T, Richardson TI, Herdendorf TJ, Kotzé TJ, O’Meara TR, Corson TW, Hermle T, Ogunwa TH, Lan T, Su T, Banjo T, O’Mara TA, Chou T, Chou TF, Baumann U, Desai UR, Pai VP, Thai VC, Tandon V, Banerji V, Robinson VL, Gunasekharan V, Namasivayam V, Segers VFM, Maranda V, Dolce V, Maltarollo VG, Scoffone VC, Woods VA, Ronchi VP, Van Hung Le V, Clayton WB, Lowther WT, Houry WA, Li W, Tang W, Zhang W, Van Voorhis WC, Donaldson WA, Hahn WC, Kerr WG, Gerwick WH, Bradshaw WJ, Foong WE, Blanchet X, Wu X, Lu X, Qi X, Xu X, Yu X, Qin X, Wang X, Yuan X, Zhang X, Zhang YJ, Hu Y, Aldhamen YA, Chen Y, Li Y, Sun Y, Zhu Y, Gupta YK, Pérez-Pertejo Y, Li Y, Tang Y, He Y, Tse-Dinh YC, Sidorova YA, Yen Y, Li Y, Frangos ZJ, Chung Z, Su Z, Wang Z, Zhang Z, Liu Z, Inde Z, Artía Z, Heifets A. AI is a viable alternative to high throughput screening: a 318-target study. Sci Rep 2024; 14:7526. [PMID: 38565852 PMCID: PMC10987645 DOI: 10.1038/s41598-024-54655-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 02/15/2024] [Indexed: 04/04/2024] Open
Abstract
High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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17
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Tang Y, Moretti R, Meiler J. Recent Advances in Automated Structure-Based De Novo Drug Design. J Chem Inf Model 2024; 64:1794-1805. [PMID: 38485516 DOI: 10.1021/acs.jcim.4c00247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.
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Affiliation(s)
- Yidan Tang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
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18
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Weller JA, Rohs R. DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.573155. [PMID: 38187658 PMCID: PMC10769420 DOI: 10.1101/2023.12.22.573155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Rapid advancement in the computational methods of structure-based drug design has led to their widespread adoption as key tools in the early drug development process. Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a significant impact on the success of early drug design efforts. However, screening-based methods are limited in their scalability due to computational limits and the sheer scale of drug-like space. An approach within the quickly evolving field of artificial intelligence (AI), deep generative modeling, is extending the reach of molecular design beyond classical methods by learning the fundamental intra- and inter-molecular relationships in drug-target systems from existing data. In this work we introduce DrugHIVE, a deep hierarchical structure-based generative model that enables fine-grained control over molecular generation. Our model outperforms state of the art autoregressive and diffusion-based methods on common benchmarks and in speed of generation. Here, we demonstrate DrugHIVEs capacity to accelerate a wide range of common drug design tasks such as de novo generation, molecular optimization, scaffold hopping, linker design, and high throughput pattern replacement. Our method is highly scalable and can be applied to high confidence AlphaFold predicted receptors, extending our ability to generate high quality drug-like molecules to a majority of the unsolved human proteome.
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19
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Meeks KR, Ji J, Protopopov MV, Tarkhanova OO, Moroz YS, Tanner JJ. Novel Fragment Inhibitors of PYCR1 from Docking-Guided X-ray Crystallography. J Chem Inf Model 2024; 64:1704-1718. [PMID: 38411104 PMCID: PMC11058006 DOI: 10.1021/acs.jcim.3c01879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The proline biosynthetic enzyme Δ1-pyrroline-5-carboxylate (P5C) reductase 1 (PYCR1) is one of the most consistently upregulated enzymes across multiple cancer types and central to the metabolic rewiring of cancer cells. Herein, we describe a fragment-based, structure-first approach to the discovery of PYCR1 inhibitors. Thirty-seven fragment-like carboxylic acids in the molecular weight range of 143-289 Da were selected from docking and then screened using X-ray crystallography as the primary assay. Strong electron density was observed for eight compounds, corresponding to a crystallographic hit rate of 22%. The fragments are novel compared to existing proline analog inhibitors in that they block both the P5C substrate pocket and the NAD(P)H binding site. Four hits showed inhibition of PYCR1 in kinetic assays, and one has lower apparent IC50 than the current best proline analog inhibitor. These results show proof-of-concept for our inhibitor discovery approach and provide a basis for fragment-to-lead optimization.
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Affiliation(s)
- Kaylen R Meeks
- Department of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States
| | - Juan Ji
- Department of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States
| | | | - Olga O Tarkhanova
- Chemspace LLC, 85 Chervonotkatska Street, Suite 1, Kyïv 02094, Ukraine
| | - Yurii S Moroz
- Chemspace LLC, 85 Chervonotkatska Street, Suite 1, Kyïv 02094, Ukraine
- Department of Chemistry, Taras Shevchenko National University of Kyïv, Kyïv 01601, Ukraine
| | - John J Tanner
- Department of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States
- Department of Chemistry, University of Missouri, Columbia, Missouri 65211, United States
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20
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Naumchyk V, Andriashvili VA, Radchenko DS, Dudenko D, Moroz YS, Tolmachev AA, Zhersh S, Grygorenko OO. S NAr or Sulfonylation? Chemoselective Amination of Halo(het)arene Sulfonyl Halides for Synthetic Applications and Ultralarge Compound Library Design. J Org Chem 2024; 89:3161-3183. [PMID: 38383160 DOI: 10.1021/acs.joc.3c02636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
The chemoselectivity of halo(het)arene sulfonyl halide aminations is studied thoroughly under parallel synthesis conditions, and the scope and limitations of the method are established. It is shown that SNAr-reactive sulfonyl halides typically undergo sulfonamide synthesis during the first step; the second amination is also possible provided that the SNAr-active center is sufficiently reactive. On the contrary, sulfonyl fluorides bearing an arylating moiety undergo selective transformation at the latter reactive center under proper control. Further sulfur-fluoride exchange (SuFEx) is also possible, which can be especially valuable for some sulfonyl halide classes. The developed two-step parallel double amination protocol provides access to a 6.67-billion compound synthetically tractable REAL-type chemical space (76% expected synthesis success rate).
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Affiliation(s)
- Vasyl Naumchyk
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Vladyslav A Andriashvili
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | | | - Dmytro Dudenko
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
| | - Yurii S Moroz
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
- Chemspace, Winston Churchill Street 85, Kyïv 02094, Ukraine
| | - Andrey A Tolmachev
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Serhii Zhersh
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Oleksandr O Grygorenko
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
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21
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Draper MR, Waterman A, Dannatt JE, Patel P. Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge. Phys Chem Chem Phys 2024; 26:7907-7919. [PMID: 38376855 PMCID: PMC10938873 DOI: 10.1039/d3cp04140a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log Ptol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind submissions yield mean unsigned errors (MUE) ranging from 1.53-2.93 log Ptol/w units. The MUEs were reduced to 1.00 log Ptol/w for the QM methods. While MM and ML methods outperformed DFT approaches for challenge molecules with fewer rotational degrees of freedom, they suffered for the larger molecules in this dataset. Overall, DFT functionals paired with a triple-ζ basis set were the simplest and most effective tool to obtain quantitatively accurate partition coefficients.
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Affiliation(s)
- Michael R Draper
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | - Asa Waterman
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | | | - Prajay Patel
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
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22
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Kaufman B, Williams EC, Underkoffler C, Pederson R, Mardirossian N, Watson I, Parkhill J. COATI: Multimodal Contrastive Pretraining for Representing and Traversing Chemical Space. J Chem Inf Model 2024; 64:1145-1157. [PMID: 38316665 DOI: 10.1021/acs.jcim.3c01753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Creating a successful small molecule drug is a challenging multiparameter optimization problem in an effectively infinite space of possible molecules. Generative models have emerged as powerful tools for traversing data manifolds composed of images, sounds, and text and offer an opportunity to dramatically improve the drug discovery and design process. To create generative optimization methods that are more useful than brute-force molecular generation and filtering via virtual screening, we propose that four integrated features are necessary: large, quantitative data sets of molecular structure and activity, an invertible vector representation of realistic accessible molecules, smooth and differentiable regressors that quantify uncertainty, and algorithms to simultaneously optimize properties of interest. Over the course of 12 months, Terray Therapeutics has collected a data set of 2 billion quantitative binding measurements of small molecules to therapeutic targets, which directly motivates multiparameter generative optimization of molecules conditioned on these data. To this end, we present contrastive optimization for accelerated therapeutic inference (COATI), a pretrained, multimodal encoder-decoder model of druglike chemical space. COATI is constructed without any human biasing of features, using contrastive learning from text and 3D representations of molecules to allow for downstream use with structural models. We demonstrate that COATI possesses many of the desired properties of universal molecular embedding: fixed-dimension, invertibility, autoencoding, accurate regression, and low computation cost. Finally, we present a novel metadynamics algorithm for generative optimization using a small subset of our proprietary data collected for a model protein, carbonic anhydrase, designing molecules that satisfy the multiparameter optimization task of potency, solubility, and drug likeness. This work sets the stage for fully integrated generative molecular design and optimization for small molecules.
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Affiliation(s)
- Benjamin Kaufman
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Edward C Williams
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Carl Underkoffler
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Ryan Pederson
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Narbe Mardirossian
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Ian Watson
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - John Parkhill
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
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23
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Tandi M, Tripathi N, Gaur A, Gopal B, Sundriyal S. Curation and cheminformatics analysis of a Ugi-reaction derived library (URDL) of synthetically tractable small molecules for virtual screening application. Mol Divers 2024; 28:37-50. [PMID: 36574164 DOI: 10.1007/s11030-022-10588-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022]
Abstract
Virtual screening (VS) is an important approach in drug discovery and relies on the availability of a virtual library of synthetically tractable molecules. Ugi reaction (UR) represents an important multi-component reaction (MCR) that reliably produces a peptidomimetic scaffold. Recent literature shows that a tactically assembled Ugi adduct can be subjected to further chemical modifications to yield a variety of rings and scaffolds, thus, renewing the interest in this old reaction. Given the reliability and efficiency of UR, we collated an UR derived library (URDL) of small molecules (total = 5773) for VS. The synthesis of the majority of URDL molecules may be carried out in 1-2 pots in a time and cost-effective manner. The detailed analysis of the average property and chemical space of URDL was also carried out using the open-source Datawarrior program. The comparison with FDA-approved oral drugs and inhibitors of protein-protein interactions (iPPIs) suggests URDL molecules are 'clean', drug-like, and conform to a structurally distinct space from the other two categories. The average physicochemical properties of compounds in the URDL library lie closer to iPPI molecules than oral drugs thus suggesting that the URDL resource can be applied to discover novel iPPI molecules. The URDL molecules consist of diverse ring systems, many of which have not been exploited yet for drug design. Thus, URDL represents a small virtual library of drug-like molecules with unexplored chemical space designed for VS. The structures of all molecules of URDL, oral drugs, and iPPI compounds are being made freely accessible as supplementary information for broader application.
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Affiliation(s)
- Mukesh Tandi
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Nancy Tripathi
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Animesh Gaur
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | | | - Sandeep Sundriyal
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India.
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24
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Melancon K, Pliushcheuskaya P, Meiler J, Künze G. Targeting ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 2024; 16:1336004. [PMID: 38249296 PMCID: PMC10796734 DOI: 10.3389/fnmol.2023.1336004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.
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Affiliation(s)
- Kortney Melancon
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
| | - Georg Künze
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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25
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Olmedo DA, Durant-Archibold AA, López-Pérez JL, Medina-Franco JL. Design and Diversity Analysis of Chemical Libraries in Drug Discovery. Comb Chem High Throughput Screen 2024; 27:502-515. [PMID: 37409545 DOI: 10.2174/1386207326666230705150110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/30/2023] [Accepted: 05/30/2023] [Indexed: 07/07/2023]
Abstract
Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.
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Affiliation(s)
- Dionisio A Olmedo
- Centro de Investigaciones Farmacognósticas de la Flora Panameña (CIFLORPAN), Facultad de Farmacia, Universidad de Panamá, Ciudad de Panamá, Apartado, 0824-00178, Panamá
- Sistema Nacional de Investigación (SNI), Secretaria Nacional de Ciencia, Tecnología e Innovación (SENACYT), Ciudad del Saber, Clayton, Panamá
| | - Armando A Durant-Archibold
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Apartado, 0843-01103, Panamá
- Departamento de Bioquímica, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - José Luis López-Pérez
- CESIFAR, Departamento de Farmacología, Facultad de Medicina, Universidad de Panamá, Ciudad de Panamá, Panamá
- Departamento de Ciencias Farmacéuticas, Facultad de Farmacia, Universidad de Salamanca, Avda. Campo Charro s/n, 37071 Salamanca, España
| | - José Luis Medina-Franco
- DIFACQUIM Grupo de Investigación, Departamento de Farmacia, Escuela de Química, Universidad Nacional Autónoma de México, Ciudad de México, Apartado, 04510, México
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26
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Gahbauer S, DeLeon C, Braz JM, Craik V, Kang HJ, Wan X, Huang XP, Billesbølle CB, Liu Y, Che T, Deshpande I, Jewell M, Fink EA, Kondratov IS, Moroz YS, Irwin JJ, Basbaum AI, Roth BL, Shoichet BK. Docking for EP4R antagonists active against inflammatory pain. Nat Commun 2023; 14:8067. [PMID: 38057319 PMCID: PMC10700596 DOI: 10.1038/s41467-023-43506-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 11/12/2023] [Indexed: 12/08/2023] Open
Abstract
The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.
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Affiliation(s)
- Stefan Gahbauer
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Chelsea DeLeon
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
| | - Joao M Braz
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Veronica Craik
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Hye Jin Kang
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, South Korea
| | - Xiaobo Wan
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Xi-Ping Huang
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
| | - Christian B Billesbølle
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Yongfeng Liu
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
| | - Tao Che
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
- Center of Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ishan Deshpande
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Madison Jewell
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Elissa A Fink
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ivan S Kondratov
- Enamine Ltd., Kyiv, Ukraine
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Yurii S Moroz
- Chemspace LLC, Kyiv, Ukraine
- National Taras Shevchenko University of Kyiv, Kyiv, Ukraine
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Allan I Basbaum
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA.
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA.
- National Institute of Mental Health Psychoactive Drug Screening Program, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA.
- Division of Chemical Biology and Medicinal Chemistry, University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, NC, 27514, USA.
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA.
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27
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Tran-Nguyen VK, Junaid M, Simeon S, Ballester PJ. A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 2023; 18:3460-3511. [PMID: 37845361 DOI: 10.1038/s41596-023-00885-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/03/2023] [Indexed: 10/18/2023]
Abstract
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.
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Affiliation(s)
| | - Muhammad Junaid
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
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28
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Buehler Y, Reymond JL. Expanding Bioactive Fragment Space with the Generated Database GDB-13s. J Chem Inf Model 2023; 63:6239-6248. [PMID: 37722101 PMCID: PMC10598793 DOI: 10.1021/acs.jcim.3c01096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Indexed: 09/20/2023]
Abstract
Identifying innovative fragments for drug design can help medicinal chemistry address new targets and overcome the limitations of the classical molecular series. By deconstructing molecules into ring fragments (RFs, consisting of ring atoms plus ring-adjacent atoms) and acyclic fragments (AFs, consisting of only acyclic atoms), we find that public databases of molecules (i.e., ZINC and PubChem) and natural products (i.e., COCONUT) contain mostly RFs and AFs of up to 13 atoms. We also find that many RFs and AFs are enriched in bioactive vs inactive compounds from ChEMBL. We then analyze the generated database GDB-13s, which enumerates 99 million possible molecules of up to 13 atoms, for RFs and AFs resembling ChEMBL bioactive RFs and AFs. This analysis reveals a large number of novel RFs and AFs that are structurally simple, have favorable synthetic accessibility scores, and represent opportunities for synthetic chemistry to contribute to drug innovation in the context of fragment-based drug discovery.
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Affiliation(s)
- Ye Buehler
- Department of Chemistry,
Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry,
Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
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29
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Abstract
Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.
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Affiliation(s)
- Christoph Gorgulla
- Harvard Medical School and Physics Department, Harvard University, Boston, Massachusetts, USA;
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Current affiliation: Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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30
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Chen L, Fan Z, Chang J, Yang R, Hou H, Guo H, Zhang Y, Yang T, Zhou C, Sui Q, Chen Z, Zheng C, Hao X, Zhang K, Cui R, Zhang Z, Ma H, Ding Y, Zhang N, Lu X, Luo X, Jiang H, Zhang S, Zheng M. Sequence-based drug design as a concept in computational drug design. Nat Commun 2023; 14:4217. [PMID: 37452028 PMCID: PMC10349078 DOI: 10.1038/s41467-023-39856-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 06/27/2023] [Indexed: 07/18/2023] Open
Abstract
Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.
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Affiliation(s)
- Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Zisheng Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China
| | - Jie Chang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Ruirui Yang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China
| | - Hui Hou
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Hao Guo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Yinghui Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Tianbiao Yang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Chenmao Zhou
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Qibang Sui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Zhengyang Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Chen Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Xinyue Hao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Keke Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Rongrong Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Zehong Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Hudson Ma
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Yiluan Ding
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Naixia Zhang
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Xiaojie Lu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Sub-lane Xiangshan, Hangzhou, 310024, China
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China.
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Sub-lane Xiangshan, Hangzhou, 310024, China.
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Thomson TM. On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front Pharmacol 2023; 14:1207559. [PMID: 37426821 PMCID: PMC10324963 DOI: 10.3389/fphar.2023.1207559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Timothy M. Thomson
- Institute for Molecular Biology (IBMB-CSIC), Barcelona, Spain
- CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
- Universidad Peruana Cayetano Heredia, Lima, Peru
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Bhat D, Malacaria E, Biagi L, Razzaghi M, Honda M, Hobbs K, Hengel S, Pichierri P, Spies M, Spies M. Therapeutic disruption of RAD52-ssDNA complexation via novel drug-like inhibitors. NAR Cancer 2023; 5:zcad018. [PMID: 37139244 PMCID: PMC10150327 DOI: 10.1093/narcan/zcad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/09/2023] [Accepted: 04/14/2023] [Indexed: 05/05/2023] Open
Abstract
RAD52 protein is a coveted target for anticancer drug discovery. Similar to poly-ADP-ribose polymerase (PARP) inhibitors, pharmacological inhibition of RAD52 is synthetically lethal with defects in genome caretakers BRCA1 and BRCA2 (∼25% of breast and ovarian cancers). Emerging structure activity relationships for RAD52 are complex, making it challenging to transform previously identified disruptors of the RAD52-ssDNA interaction into drug-like leads using traditional medicinal chemistry approaches. Using pharmacophoric informatics on the RAD52 complexation by epigallocatechin (EGC), and the Enamine in silico REAL database, we identified six distinct chemical scaffolds that occupy the same physical space on RAD52 as EGC. All six were RAD52 inhibitors (IC50 ∼23-1200 μM) with two of the compounds (Z56 and Z99) selectively killing BRCA-mutant cells and inhibiting cellular activities of RAD52 at micromolar inhibitor concentrations. While Z56 had no effect on the ssDNA-binding protein RPA and was toxic to BRCA-mutant cells only, Z99 inhibited both proteins and displayed toxicity towards BRCA-complemented cells. Optimization of the Z99 scaffold resulted in a set of more powerful and selective inhibitors (IC50 ∼1.3-8 μM), which were only toxic to BRCA-mutant cells. RAD52 complexation by Z56, Z99 and its more specific derivatives provide a roadmap for next generation of cancer therapeutics.
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Affiliation(s)
- Divya S Bhat
- Department of Biochemistry, University of Iowa Carver College of Medicine, 51 Newton Road, Iowa City, IA 52242, USA
| | - Eva Malacaria
- Mechanisms, Biomarkers and Models Section, Department of Environment and Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Ludovica Di Biagi
- Mechanisms, Biomarkers and Models Section, Department of Environment and Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Mortezaali Razzaghi
- Department of Biochemistry, University of Iowa Carver College of Medicine, 51 Newton Road, Iowa City, IA 52242, USA
| | - Masayoshi Honda
- Department of Biochemistry, University of Iowa Carver College of Medicine, 51 Newton Road, Iowa City, IA 52242, USA
| | - Kathryn F Hobbs
- Department of Biochemistry, University of Iowa Carver College of Medicine, 51 Newton Road, Iowa City, IA 52242, USA
- Division of Medicinal and Natural Products Chemistry, Department of Pharmaceutical Sciences and Experimental Therapeutics, The University of Iowa, Iowa City, IA 52242, USA
| | - Sarah R Hengel
- Department of Biochemistry, University of Iowa Carver College of Medicine, 51 Newton Road, Iowa City, IA 52242, USA
| | - Pietro Pichierri
- Mechanisms, Biomarkers and Models Section, Department of Environment and Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - M Ashley Spies
- Department of Biochemistry, University of Iowa Carver College of Medicine, 51 Newton Road, Iowa City, IA 52242, USA
- Division of Medicinal and Natural Products Chemistry, Department of Pharmaceutical Sciences and Experimental Therapeutics, The University of Iowa, Iowa City, IA 52242, USA
- Naturis Informatika LLC, 401 Mullin Ave., Iowa City, IA 52246, USA
| | - Maria Spies
- Department of Biochemistry, University of Iowa Carver College of Medicine, 51 Newton Road, Iowa City, IA 52242, USA
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33
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Essegian DJ, Chavez V, Khurshid R, Merchan JR, Schürer SC. AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK. PLoS Comput Biol 2023; 19:e1010263. [PMID: 37235579 PMCID: PMC10249896 DOI: 10.1371/journal.pcbi.1010263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/08/2023] [Accepted: 04/13/2023] [Indexed: 05/28/2023] Open
Abstract
PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes.
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Affiliation(s)
- Derek J. Essegian
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Valery Chavez
- Department of Medicine, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Rabia Khurshid
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Jaime R. Merchan
- Department of Medicine, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, United States of America
- Department of Medicine, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida, United States of America
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34
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Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 135] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
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Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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35
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Rogers DM, Agarwal R, Vermaas JV, Smith MD, Rajeshwar RT, Cooper C, Sedova A, Boehm S, Baker M, Glaser J, Smith JC. SARS-CoV2 billion-compound docking. Sci Data 2023; 10:173. [PMID: 36977690 PMCID: PMC10044124 DOI: 10.1038/s41597-023-01984-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 01/24/2023] [Indexed: 03/30/2023] Open
Abstract
This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.
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Affiliation(s)
- David M Rogers
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Rupesh Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Josh V Vermaas
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
| | - Micholas Dean Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Rajitha T Rajeshwar
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Connor Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Biological Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Ada Sedova
- Biological Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Swen Boehm
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Matthew Baker
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jens Glaser
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA.
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36
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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37
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Grasso D, Galderisi S, Santucci A, Bernini A. Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology. Int J Mol Sci 2023; 24:ijms24065819. [PMID: 36982893 PMCID: PMC10054308 DOI: 10.3390/ijms24065819] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.
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Affiliation(s)
- Daniela Grasso
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Silvia Galderisi
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Andrea Bernini
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
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38
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Sala D, Batebi H, Ledwitch K, Hildebrand PW, Meiler J. Targeting in silico GPCR conformations with ultra-large library screening for hit discovery. Trends Pharmacol Sci 2023; 44:150-161. [PMID: 36669974 PMCID: PMC9974811 DOI: 10.1016/j.tips.2022.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 01/20/2023]
Abstract
The use of deep machine learning (ML) in protein structure prediction has made it possible to easily access a large number of annotated conformations that can potentially compensate for missing experimental structures in structure-based drug discovery (SBDD). However, it is still unclear whether the accuracy of these predicted conformations is sufficient for screening chemical compounds that will effectively interact with a protein target for pharmacological purposes. In this opinion article, we examine the potential benefits and limitations of using state-annotated conformations for ultra-large library screening (ULLS) in light of the growing size of ultra-large libraries (ULLs). We believe that targeting different conformational states of common drug targets like G-protein-coupled receptors (GPCRs), which can regulate human physiology by switching between different conformations, can offer multiple advantages.
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Affiliation(s)
- D Sala
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
| | - H Batebi
- Institute of Medical Physics and Biophysics, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
| | - K Ledwitch
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - P W Hildebrand
- Institute of Medical Physics and Biophysics, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
| | - J Meiler
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany; Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA.
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39
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Potlitz F, Link A, Schulig L. Advances in the discovery of new chemotypes through ultra-large library docking. Expert Opin Drug Discov 2023; 18:303-313. [PMID: 36714919 DOI: 10.1080/17460441.2023.2171984] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (Mpro). AREAS COVERED This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel Mpro inhibitors targeting the SARS-CoV-2 virus. EXPERT OPINION With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.
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Affiliation(s)
- Felix Potlitz
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Andreas Link
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Lukas Schulig
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
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40
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Schieferdecker S, Vock E. Development of Pharmacophore Models for the Important Off-Target 5-HT 2B Receptor. J Med Chem 2023; 66:1509-1521. [PMID: 36621987 DOI: 10.1021/acs.jmedchem.2c01679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Toxicity is a major cause of attrition in the development of pharmaceuticals, and the off-target effects are a frequent contributor. The 5-HT2B receptor agonism is known to be responsible for a variety of safety concerns including valvular heart disease which was the cause for the withdrawal of several compounds from the market. An early detection of potential binding to this receptor is thus desirable. Herein, we present the identification of key amino acid residues in the active site of 5-HT2B by molecular dynamics simulations, the development of pharmacophore models and their performance on in-house data, and a structurally highly diverse subset of Enamine REAL labeled for 5-HT2B activity by a machine learning model. These models may be used as filters employed on screening compound sets for the early filtration of compounds with potential 5-HT2B off-target liabilities.
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Affiliation(s)
- Sebastian Schieferdecker
- Department of Nonclinical Drug Safety, Germany, Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach88397, Germany
| | - Esther Vock
- Department of Nonclinical Drug Safety, Germany, Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach88397, Germany
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41
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Gusev F, Gutkin E, Kurnikova MG, Isayev O. Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling. J Chem Inf Model 2023; 63:583-594. [PMID: 36599125 DOI: 10.1021/acs.jcim.2c01052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE). Thermodynamics integration (TI) based on molecular dynamics (MD) simulations is a BFE calculation method capable of acquiring accurate BFE, but it is computationally expensive and time-consuming. In this work, we have developed an efficient automated workflow for identifying compounds with the lowest BFE among thousands of congeneric ligands, which requires only hundreds of TI calculations. Automated machine learning (AutoML) orchestrated by active learning (AL) in an AL-AutoML workflow allows unbiased and efficient search for a small set of best-performing molecules. We have applied this workflow to select inhibitors of the SARS-CoV-2 papain-like protease and were able to find 133 compounds with improved binding affinity, including 16 compounds with better than 100-fold binding affinity improvement. We obtained a hit rate that outperforms that expected of traditional expert medicinal chemist-guided campaigns. Thus, we demonstrate that the combination of AL and AutoML with free energy simulations provides at least 20× speedup relative to the naïve brute force approaches.
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Affiliation(s)
- Filipp Gusev
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Evgeny Gutkin
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Maria G Kurnikova
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
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42
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Bai N, Adeshina Y, Bychkov I, Xia Y, Gowthaman R, Miller SA, Gupta AK, Johnson DK, Lan L, Golemis EA, Makhov PB, Xu L, Pillai MM, Boumber Y, Karanicolas J. Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry. RESEARCH SQUARE 2023:rs.3.rs-2395172. [PMID: 36711552 PMCID: PMC9882606 DOI: 10.21203/rs.3.rs-2395172/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.
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Affiliation(s)
- Nan Bai
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
| | - Yusuf Adeshina
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Center for Computational Biology, University of Kansas, Lawrence KS 66045
| | - Igor Bychkov
- Division of Hematology/Oncology, Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
| | - Yan Xia
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
| | - Ragul Gowthaman
- Center for Computational Biology, University of Kansas, Lawrence KS 66045
| | - Sven A. Miller
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
| | - Abhishek K. Gupta
- Section of Hematology, Yale Cancer Center, New Haven CT 06520
- Department of Pathology, Yale University School of Medicine, New Haven CT 06520
| | - David K. Johnson
- Center for Computational Biology, University of Kansas, Lawrence KS 66045
| | - Lan Lan
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
| | - Erica A. Golemis
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Department of Cancer and Cellular Biology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140
| | - Petr B. Makhov
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
| | - Liang Xu
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
- Department of Radiation Oncology, University of Kansas Medical Center, Kansas City KS 66160
| | - Manoj M. Pillai
- Section of Hematology, Yale Cancer Center, New Haven CT 06520
- Department of Pathology, Yale University School of Medicine, New Haven CT 06520
| | - Yanis Boumber
- Division of Hematology/Oncology, Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
| | - John Karanicolas
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia, PA 19140
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43
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Bai N, Adeshina Y, Bychkov I, Xia Y, Gowthaman R, Miller SA, Gupta AK, Johnson DK, Lan L, Golemis EA, Makhov PB, Xu L, Pillai MM, Boumber Y, Karanicolas J. Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.09.523326. [PMID: 36711508 PMCID: PMC9882015 DOI: 10.1101/2023.01.09.523326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.
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Affiliation(s)
- Nan Bai
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
| | - Yusuf Adeshina
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Center for Computational Biology, University of Kansas, Lawrence KS 66045
| | - Igor Bychkov
- Division of Hematology/Oncology, Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
| | - Yan Xia
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
| | - Ragul Gowthaman
- Center for Computational Biology, University of Kansas, Lawrence KS 66045
| | - Sven A. Miller
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
| | | | - David K. Johnson
- Center for Computational Biology, University of Kansas, Lawrence KS 66045
| | - Lan Lan
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
| | - Erica A. Golemis
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Department of Cancer and Cellular Biology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140
| | - Petr B. Makhov
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
| | - Liang Xu
- Department of Molecular Biosciences, University of Kansas, Lawrence KS 66045
- Department of Radiation Oncology, University of Kansas Medical Center, Kansas City KS 66160
| | - Manoj M. Pillai
- Section of Hematology, Yale Cancer Center, New Haven CT 06520
- Department of Pathology, Yale University School of Medicine, New Haven CT 06520
| | - Yanis Boumber
- Division of Hematology/Oncology, Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
| | - John Karanicolas
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia PA 19111
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia, PA 19140
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44
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Yu W, Weber DJ, MacKerell AD. Computer-Aided Drug Design: An Update. Methods Mol Biol 2023; 2601:123-152. [PMID: 36445582 PMCID: PMC9838881 DOI: 10.1007/978-1-0716-2855-3_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.
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Affiliation(s)
- Wenbo Yu
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
| | - David J Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
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45
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Perebyinis M, Rognan D. Overlap of On-demand Ultra-large Combinatorial Spaces with On-the-shelf Drug-like Libraries. Mol Inform 2023; 42:e2200163. [PMID: 36072995 DOI: 10.1002/minf.202200163] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/07/2022] [Indexed: 01/12/2023]
Abstract
On-demand combinatorial spaces are shifting paradigms in early drug discovery, by considerably increasing the searchable chemical space to several billions of compounds while securing their synthetic accessibility. We here systematically compared the on-the-shelf available drug-like chemical space (9 million compounds) to three on-demand ultra-large (ODUL) combinatorial fragment spaces (REAL, CHEMriya, GalaXi) covering 32 billion of readily accessible molecules. Surprisingly, only one space (REAL) intersects almost entirely the currently available drug-like space, suggesting that it is the only ODUL widely suitable for in-stock hit expansion. Of course, expanding a preliminary ODUL hit in the same chemical space is the best possible strategy to rapidly generate structure-activity relationships. All three spaces remain well suited to early hit finding initiatives since they all provide numerous unique scaffolds that are not described by on-the shelf collections.
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Affiliation(s)
- Mariana Perebyinis
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, F-67400, Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, F-67400, Illkirch, France
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46
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McMillan AE, Wu WWX, Nichols PL, Wanner BM, Bode JW. A vending machine for drug-like molecules - automated synthesis of virtual screening hits. Chem Sci 2022; 13:14292-14299. [PMID: 36545137 PMCID: PMC9749103 DOI: 10.1039/d2sc05182f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022] Open
Abstract
As a result of high false positive rates in virtual screening campaigns, prospective hits must be synthesised for validation. When done manually, this is a time consuming and laborious process. Large "on-demand" virtual libraries (>7 × 1012 members), suitable for preparation using capsule-based automated synthesis and commercial building blocks, were evaluated to determine their structural novelty. One sub-library, constructed from iSnAP capsules, aldehydes and amines, contains unique scaffolds with drug-like physicochemical properties. Virtual screening hits from this iSnAP library were prepared in an automated fashion for evaluation against Aedes aegypti and Phytophthora infestans. In comparison to manual workflows, this approach provided a 10-fold improvement in user efficiency. A streamlined method of relative stereochemical assignment was also devised to augment the rapid synthesis. User efficiency was further improved to 100-fold by downscaling and parallelising capsule-based chemistry on 96-well plates equipped with filter bases. This work demonstrates that automated synthesis consoles can enable the rapid and reliable preparation of attractive virtual screening hits from large virtual libraries.
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Affiliation(s)
- Angus E. McMillan
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Wilson W. X. Wu
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Paula L. Nichols
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland,Synple Chem AGKemptpark 18Kemptthal 8310Switzerland
| | | | - Jeffrey W. Bode
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
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47
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Young RJ, Flitsch SL, Grigalunas M, Leeson PD, Quinn RJ, Turner NJ, Waldmann H. The Time and Place for Nature in Drug Discovery. JACS AU 2022; 2:2400-2416. [PMID: 36465532 PMCID: PMC9709949 DOI: 10.1021/jacsau.2c00415] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/06/2022] [Indexed: 05/31/2023]
Abstract
The case for a renewed focus on Nature in drug discovery is reviewed; not in terms of natural product screening, but how and why biomimetic molecules, especially those produced by natural processes, should deliver in the age of artificial intelligence and screening of vast collections both in vitro and in silico. The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. Nature's finite number of building blocks and their interactions necessarily reduce potential numbers of structures, yet these enable expansion of chemical space with their inherent diversity of physical characteristics, pertinent to property-based design. The feasible variations on natural motifs are considered and expanded to encompass pseudo-natural products, leading to the further logical step of harnessing bioprocessing routes to access them. Together, these offer opportunities for enhancing natural mimicry, thereby bringing innovation to drug synthesis exploiting the characteristics of natural recognition processes. The potential for computational guidance to help identifying binding commonalities in the route map is a logical opportunity to enable the design of tailored molecules, with a focus on "organic/biological" rather than purely "synthetic" structures. The design and synthesis of prototype structures should pay dividends in the disposition and efficacy of the molecules, while inherently enabling greener and more sustainable manufacturing techniques.
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Affiliation(s)
| | - Sabine L. Flitsch
- Department
of Chemistry, University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Michael Grigalunas
- Department
of Chemical Biology, Max-Planck-Institute
of Molecular Physiology, Otto-Hahn Strasse 11, 44227 Dortmund, Germany
| | - Paul D. Leeson
- Paul
Leeson Consulting Limited, The Malt House, Main Street, Congerstone, Nuneaton, Warwickshire CV13 6LZ, U.K.
| | - Ronald J. Quinn
- Griffith
Institute for Drug Discovery, Griffith University, Nathan, Queensland 4111, Australia
| | - Nicholas J. Turner
- Department
of Chemistry, University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Herbert Waldmann
- Department
of Chemical Biology, Max-Planck-Institute
of Molecular Physiology, Otto-Hahn Strasse 11, 44227 Dortmund, Germany
- Faculty of
Chemistry and Chemical Biology, Technical
University of Dortmund, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany
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48
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Li X, Li Y, Shu J, Fu X, Wu L, Shi T, Hu W. Rh 2(Ph 3COO) 3(OAc)/Chiral Phosphoric Acid Cocatalyzed N-Alkyl Imines-Involved Multicomponent Reactions Yielding N-(Anthrancen-9-ylmethyl) Isoserines as Drug Intermediates. Org Lett 2022; 24:8633-8638. [DOI: 10.1021/acs.orglett.2c03368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Xinglin Li
- Guangdong Chiral Drug Engineering Laboratory, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yukai Li
- Guangdong Chiral Drug Engineering Laboratory, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Jirong Shu
- Guangdong Chiral Drug Engineering Laboratory, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Xiang Fu
- Guangdong Chiral Drug Engineering Laboratory, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Linna Wu
- Guangdong Chiral Drug Engineering Laboratory, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Taoda Shi
- Guangdong Chiral Drug Engineering Laboratory, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Wenhao Hu
- Guangdong Chiral Drug Engineering Laboratory, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
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49
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Kontoyianni M. Library size in virtual screening: is it truly a number's game? Expert Opin Drug Discov 2022; 17:1177-1179. [PMID: 36196482 DOI: 10.1080/17460441.2022.2130244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University Edwardsville, Edwardsville, IL, USA
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50
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Korshunova M, Huang N, Capuzzi S, Radchenko DS, Savych O, Moroz YS, Wells CI, Willson TM, Tropsha A, Isayev O. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds. Commun Chem 2022; 5:129. [PMID: 36697952 PMCID: PMC9814657 DOI: 10.1038/s42004-022-00733-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 09/12/2022] [Indexed: 01/28/2023] Open
Abstract
Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches.
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Affiliation(s)
- Maria Korshunova
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA. .,Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Niles Huang
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dmytro S Radchenko
- Enamine Ltd, 78 Chervonotkatska Street, Kyiv, 02094, Ukraine.,Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine
| | - Olena Savych
- Enamine Ltd, 78 Chervonotkatska Street, Kyiv, 02094, Ukraine
| | - Yuriy S Moroz
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine.,Chemspace LLC, Chervonotkatska Street 85, Suite 1, Kyiv, 02094, Ukraine
| | - Carrow I Wells
- Structual Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Timothy M Willson
- Structual Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Olexandr Isayev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA. .,Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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