1
|
Miljković F, Bajorath J. Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence. Mol Pharm 2024. [PMID: 39240193 DOI: 10.1021/acs.molpharmaceut.4c00659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.
Collapse
Affiliation(s)
- Filip Miljković
- Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, SE-43183 Gothenburg, Sweden
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, Lamarr Institute for Machine Learning and Artificial Intelligence, LIMES Program Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany
| |
Collapse
|
2
|
Weller J, Rohs R. Structure-Based Drug Design with a Deep Hierarchical Generative Model. J Chem Inf Model 2024; 64:6450-6463. [PMID: 39058534 PMCID: PMC11350878 DOI: 10.1021/acs.jcim.4c01193] [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: 07/08/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
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 notable impact on early drug design efforts. Yet screening-based methods still face scalability limits, due to computational constraints and the sheer scale of drug-like space. Machine learning approaches are overcoming these limitations by learning the fundamental intra- and intermolecular relationships in drug-target systems from existing data. Here, we introduce DrugHIVE, a deep hierarchical variational autoencoder that outperforms state-of-the-art autoregressive and diffusion-based methods in both speed and performance on common generative benchmarks. DrugHIVE's hierarchical design enables improved control over molecular generation. Its capabilities include dramatically increasing virtual screening efficiency and accelerating a wide range of common drug design tasks, including de novo generation, molecular optimization, scaffold hopping, linker design, and high-throughput pattern replacement. Our highly scalable method can even be applied to receptors with high-confidence AlphaFold-predicted structures, extending the ability to generate high-quality drug-like molecules to a majority of the unsolved human proteome.
Collapse
Affiliation(s)
- Jesse
A. Weller
- Department
of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, United States
- Department
of Physics and Astronomy, University of
Southern California, Los Angeles, California 90089, United States
| | - Remo Rohs
- Department
of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, United States
- Department
of Physics and Astronomy, University of
Southern California, Los Angeles, California 90089, United States
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
- Thomas
Lord Department of Computer Science, University
of Southern California, Los Angeles, California 90089, United States
| |
Collapse
|
3
|
Zhang Y, Mastouri M, Zhang Y. Accelerating drug discovery, development, and clinical trials by artificial intelligence. MED 2024:S2666-6340(24)00308-8. [PMID: 39173629 DOI: 10.1016/j.medj.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/21/2024] [Accepted: 07/25/2024] [Indexed: 08/24/2024]
Abstract
Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assisted drug development, particularly focusing on small molecules, RNA, and antibodies. Moreover, this paper elucidates the current integration of AI methodologies within the industrial drug development framework. This encompasses a detailed examination of the industry-standard drug development process, supplemented by a review of medications presently undergoing clinical trials. Conclusively, the paper tackles a predominant obstacle within the AI pharmaceutical sector: the absence of AI-conceived drugs receiving approval. This paper also advocates for the adoption of large language models and diffusion models as a viable strategy to surmount this challenge. This review not only underscores the significant potential of AI in drug discovery but also deliberates on the challenges and prospects within this dynamically progressing field.
Collapse
Affiliation(s)
- Yilun Zhang
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China; School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China
| | - Mohamed Mastouri
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China
| | - Yang Zhang
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China.
| |
Collapse
|
4
|
Liu Y, Xu C, Yang X, Zhang Y, Chen Y, Liu H. Application progress of deep generative models in de novo drug design. Mol Divers 2024:10.1007/s11030-024-10942-5. [PMID: 39097862 DOI: 10.1007/s11030-024-10942-5] [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: 05/24/2024] [Accepted: 07/16/2024] [Indexed: 08/05/2024]
Abstract
The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.
Collapse
Affiliation(s)
- Yingxu Liu
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Chengcheng Xu
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Xinyi Yang
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Yanmin Zhang
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Yadong Chen
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Haichun Liu
- School of Science, China Pharmaceutical University, Nanjing, 210009, China.
| |
Collapse
|
5
|
Yu Y, Hu Y, Yan H, Zeng X, Yang H, Xu L, Sheng R. Discovery of 5-(1-benzyl-1H-imidazol-4-yl)-1,2,4-oxadiazole derivatives as novel RIPK1 inhibitors via structure-based virtual screening. Drug Dev Res 2024; 85:e22235. [PMID: 39021343 DOI: 10.1002/ddr.22235] [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: 04/13/2024] [Revised: 06/05/2024] [Accepted: 06/26/2024] [Indexed: 07/20/2024]
Abstract
RIPK1 plays a key role in necroptosis and is associated with various inflammatory diseases. Using structure-based virtual screening, a novel hit with 5-(1-benzyl-1H-imidazol-4-yl)-1,2,4-oxadiazole scaffold was identified as an RIPK1 inhibitor with an IC50 value of 1.3 μM. Further structure-activity relationship study was performed based on similarity research and biological evaluation. The molecular dynamics simulation of compound 2 with RIPK1 indicated that it may act as a type II kinase inhibitor. This study provides a highly efficient way to discover novel scaffold RIPK1 inhibitors for further development.
Collapse
Affiliation(s)
- Yanzhen Yu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunzhen Hu
- Department of Clinical Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Huihui Yan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xin Zeng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Haodong Yang
- Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Rong Sheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, China
| |
Collapse
|
6
|
Guo J, Schwaller P. Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning. JACS AU 2024; 4:2160-2172. [PMID: 38938817 PMCID: PMC11200228 DOI: 10.1021/jacsau.4c00066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 06/29/2024]
Abstract
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose significant computational cost. Consequently, designing molecules that are optimized for such oracles cannot be achieved under a practical computational budget. Molecular generative models based on simplified molecular-input line-entry system (SMILES) have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the practical molecular optimization (PMO) benchmark. Here, we first show that experience replay drastically improves the performance of multiple previously proposed algorithms. Next, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task, a drug discovery case study requiring both exploration and exploitation, and a materials design case study optimizing explicitly for quantum-mechanical properties. Our method achieves a new state-of-the-art in sample-efficient de novo molecular design, outperforming all of the previously reported methods. The code is available at https://github.com/schwallergroup/augmented_memory.
Collapse
Affiliation(s)
- Jeff Guo
- Laboratory
of Artificial Chemical Intelligence (LIAC), Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Philippe Schwaller
- Laboratory
of Artificial Chemical Intelligence (LIAC), Institut des Sciences
et Ingénierie Chimiques, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| |
Collapse
|
7
|
Crouzet A, Lopez N, Riss Yaw B, Lepelletier Y, Demange L. The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang? Molecules 2024; 29:2716. [PMID: 38930784 PMCID: PMC11206022 DOI: 10.3390/molecules29122716] [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: 03/29/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.
Collapse
Affiliation(s)
- Aurore Crouzet
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
| | - Nicolas Lopez
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- ENOES, 62 Rue de Miromesnil, 75008 Paris, France
- Unité Mixte de Recherche «Institut de Physique Théorique (IPhT)» CEA-CNRS, UMR 3681, Bat 774, Route de l’Orme des Merisiers, 91191 St Aubin-Gif-sur-Yvette, France
| | - Benjamin Riss Yaw
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| | - Yves Lepelletier
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- Université Paris Cité, Imagine Institute, 24 Boulevard Montparnasse, 75015 Paris, France
- INSERM UMR 1163, Laboratory of Cellular and Molecular Basis of Normal Hematopoiesis and Hematological Disorders: Therapeutical Implications, 24 Boulevard Montparnasse, 75015 Paris, France
| | - Luc Demange
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| |
Collapse
|
8
|
Mousavi H, Rimaz M, Zeynizadeh B. Practical Three-Component Regioselective Synthesis of Drug-Like 3-Aryl(or heteroaryl)-5,6-dihydrobenzo[ h]cinnolines as Potential Non-Covalent Multi-Targeting Inhibitors To Combat Neurodegenerative Diseases. ACS Chem Neurosci 2024; 15:1828-1881. [PMID: 38647433 DOI: 10.1021/acschemneuro.4c00055] [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: 04/25/2024] Open
Abstract
Neurodegenerative diseases (NDs) are one of the prominent health challenges facing contemporary society, and many efforts have been made to overcome and (or) control it. In this research paper, we described a practical one-pot two-step three-component reaction between 3,4-dihydronaphthalen-1(2H)-one (1), aryl(or heteroaryl)glyoxal monohydrates (2a-h), and hydrazine monohydrate (NH2NH2•H2O) for the regioselective preparation of some 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnoline derivatives (3a-h). After synthesis and characterization of the mentioned cinnolines (3a-h), the in silico multi-targeting inhibitory properties of these heterocyclic scaffolds have been investigated upon various Homo sapiens-type enzymes, including hMAO-A, hMAO-B, hAChE, hBChE, hBACE-1, hBACE-2, hNQO-1, hNQO-2, hnNOS, hiNOS, hPARP-1, hPARP-2, hLRRK-2(G2019S), hGSK-3β, hp38α MAPK, hJNK-3, hOGA, hNMDA receptor, hnSMase-2, hIDO-1, hCOMT, hLIMK-1, hLIMK-2, hRIPK-1, hUCH-L1, hPARK-7, and hDHODH, which have confirmed their functions and roles in the neurodegenerative diseases (NDs), based on molecular docking studies, and the obtained results were compared with a wide range of approved drugs and well-known (with IC50, EC50, etc.) compounds. In addition, in silico ADMET prediction analysis was performed to examine the prospective drug properties of the synthesized heterocyclic compounds (3a-h). The obtained results from the molecular docking studies and ADMET-related data demonstrated that these series of 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines (3a-h), especially hit ones, can really be turned into the potent core of new drugs for the treatment of neurodegenerative diseases (NDs), and/or due to the having some reactionable locations, they are able to have further organic reactions (such as cross-coupling reactions), and expansion of these compounds (for example, with using other types of aryl(or heteroaryl)glyoxal monohydrates) makes a new avenue for designing novel and efficient drugs for this purpose.
Collapse
Affiliation(s)
- Hossein Mousavi
- Department of Organic Chemistry, Faculty of Chemistry, Urmia University, Urmia 5756151818, Iran
| | - Mehdi Rimaz
- Department of Chemistry, Payame Noor University, P.O. Box 19395-3697, Tehran 19395-3697, Iran
| | - Behzad Zeynizadeh
- Department of Organic Chemistry, Faculty of Chemistry, Urmia University, Urmia 5756151818, Iran
| |
Collapse
|
9
|
Pang C, Qiao J, Zeng X, Zou Q, Wei L. Deep Generative Models in De Novo Drug Molecule Generation. J Chem Inf Model 2024; 64:2174-2194. [PMID: 37934070 DOI: 10.1021/acs.jcim.3c01496] [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: 11/08/2023]
Abstract
The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence has exhibited promising and efficient performance for drug-like molecule generation. In particular, deep generative models achieve great success in de novo generation of drug-like molecules with desired properties, showing massive potential for novel drug discovery. In this study, we review the recent progress of molecule generation using deep generative models, mainly focusing on molecule representations, public databases, data processing tools, and advanced artificial intelligence based molecule generation frameworks. In particular, we present a comprehensive comparison of state-of-the-art deep generative models for molecule generation and a summary of commonly used molecular design strategies. We identify research gaps and challenges of molecule generation such as the need for better databases, missing 3D information in molecular representation, and the lack of high-precision evaluation metrics. We suggest future directions for molecular generation and drug discovery.
Collapse
Affiliation(s)
- Chao Pang
- School of Software, Shandong University, Jinan 250100, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250100, China
| | - Jianbo Qiao
- School of Software, Shandong University, Jinan 250100, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250100, China
| | - Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University, Changsha 410082, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250100, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250100, China
| |
Collapse
|
10
|
Dodds M, Guo J, Löhr T, Tibo A, Engkvist O, Janet JP. Sample efficient reinforcement learning with active learning for molecular design. Chem Sci 2024; 15:4146-4160. [PMID: 38487235 PMCID: PMC10935729 DOI: 10.1039/d3sc04653b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/07/2024] [Indexed: 03/17/2024] Open
Abstract
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain.
Collapse
Affiliation(s)
- Michael Dodds
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jeff Guo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Thomas Löhr
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Alessandro Tibo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| |
Collapse
|
11
|
He D, Liu Q, Mi Y, Meng Q, Xu L, Hou C, Wang J, Li N, Liu Y, Chai H, Yang Y, Liu J, Wang L, Hou Y. De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307245. [PMID: 38204214 PMCID: PMC10962488 DOI: 10.1002/advs.202307245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/05/2023] [Indexed: 01/12/2024]
Abstract
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.
Collapse
Affiliation(s)
- Dakuo He
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Qing Liu
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Yan Mi
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Qingqi Meng
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Libin Xu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Chunyu Hou
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Jinpeng Wang
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Ning Li
- School of Traditional Chinese Materia MedicaKey Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang CityShenyang Pharmaceutical UniversityShenyang110016China
| | - Yang Liu
- Key Laboratory of Structure‐Based Drug Design & Discovery of Ministry of EducationShenyang Pharmaceutical UniversityShenyang110016China
| | - Huifang Chai
- School of PharmacyGuizhou University of Traditional Chinese MedicineGuiyang550025China
| | - Yanqiu Yang
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Jingyu Liu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Lihui Wang
- Department of PharmacologyShenyang Pharmaceutical UniversityShenyang110016China
| | - Yue Hou
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| |
Collapse
|
12
|
Shen T, Guo J, Han Z, Zhang G, Liu Q, Si X, Wang D, Wu S, Xia J. AutoMolDesigner for Antibiotic Discovery: An AI-Based Open-Source Software for Automated Design of Small-Molecule Antibiotics. J Chem Inf Model 2024; 64:575-583. [PMID: 38265916 DOI: 10.1021/acs.jcim.3c01562] [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: 01/26/2024]
Abstract
Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in holistic and efficient tools specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative-deep-learning-enabled molecular generation and automated machine-learning-based antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole-cell-based antibiotic screening and design. It is open-source, thus allowing for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with a Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/10097899).
Collapse
Affiliation(s)
- Tao Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jiale Guo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zunsheng Han
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Gao Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Qingxin Liu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
- School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China
| | - Xinxin Si
- School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China
| | - Dongmei Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| |
Collapse
|
13
|
Bo W, Duan Y, Zou Y, Ma Z, Yang T, Wang P, Guo T, Fu Z, Wang J, Fan L, Liu J, Wang T, Chen L. Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors. J Chem Inf Model 2024; 64:737-748. [PMID: 38258981 DOI: 10.1021/acs.jcim.3c01818] [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: 01/24/2024]
Abstract
Deep generative models have become crucial tools in de novo drug design. In current models for multiobjective optimization in molecular generation, the scaffold diversity is limited when multiple constraints are introduced. To enhance scaffold diversity, we herein propose a local scaffold diversity-contributed generator (LSDC), which can be utilized to generate diverse lead compounds capable of satisfying multiple constraints. Compared to the state-of-the-art methods, molecules generated by LSDC exhibit greater diversity when applied to the generation of inhibitors targeting the NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3). We present 12 molecules, some of which feature previously unreported scaffolds, and demonstrate their reasonable docking binding modes. Consequently, the modification of selected scaffolds and subsequent bioactivity evaluation lead to the discovery of two potent NLRP3 inhibitors, A22 and A14, with IC50 values of 38.1 nM and 44.43 nM, respectively. And the oral bioavailability of compound A14 is very high (F is 83.09% in mice). This work contributes to the discovery of novel NLRP3 inhibitors and provides a reference for integrating AI-based generation with wet experiments.
Collapse
Affiliation(s)
- Weichen Bo
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yangqin Duan
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yurong Zou
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Ziyan Ma
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tao Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Peng Wang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tao Guo
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhiyuan Fu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea
| | - Linchuan Fan
- College of Automation, Chongqing University, Chongqing 40000, China
| | - Jie Liu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Taijin Wang
- Chengdu Zenitar Biomedical Technology Co., Ltd, Chengdu 610041, China
| | - Lijuan Chen
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- Chengdu Zenitar Biomedical Technology Co., Ltd, Chengdu 610041, China
| |
Collapse
|
14
|
Zhang L, Li Y, Tian C, Yang R, Wang Y, Xu H, Zhu Q, Chen S, Li L, Yang S. From Hit to Lead: Structure-Based Optimization of Novel Selective Inhibitors of Receptor-Interacting Protein Kinase 1 (RIPK1) for the Treatment of Inflammatory Diseases. J Med Chem 2024; 67:754-773. [PMID: 38159286 DOI: 10.1021/acs.jmedchem.3c02102] [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: 01/03/2024]
Abstract
Receptor-interacting protein kinase 1 (RIPK1) is a key regulator of cellular necroptosis, which is considered as an important therapeutic target for necroptosis-related indications. Herein, we report the structural optimization and structure-activity relationship investigations of a series of eutectic 5-substituted-indole-3-carboxamide derivatives. The prioritized compound 10b exhibited low nanomolar IC50 values against RIPK1 and showed good kinase selectivity. Based on its eutectic structure, 10b occupied both the allosteric and ATP binding pockets of RIPK1, making it a potent dual-mode inhibitor of RIPK1. In vitro, 10b had a potent protective effect against necroptosis in cells. Compound 10b also provided robust protection in a TNFα-induced systemic inflammatory response syndrome (SIRS) model and imiquimod (IMQ)-induced psoriasis model. It also showed good pharmacokinetic properties and low toxicity. Overall, 10b is a promising lead compound for drug discovery targeting RIPK1 and warrants further study.
Collapse
Affiliation(s)
- Liting Zhang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yueshan Li
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- Frontier Medical Center, Tianfu Jincheng Laboratory, Chengdu, Sichuan 610212, China
| | - Chenyu Tian
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Ruicheng Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yifei Wang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Haixing Xu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qiucheng Zhu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Shasha Chen
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Linli Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Shengyong Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- Frontier Medical Center, Tianfu Jincheng Laboratory, Chengdu, Sichuan 610212, China
| |
Collapse
|
15
|
Chandra R, Horne RI, Vendruscolo M. Bayesian Optimization in the Latent Space of a Variational Autoencoder for the Generation of Selective FLT3 Inhibitors. J Chem Theory Comput 2024; 20:469-476. [PMID: 38112559 PMCID: PMC10782437 DOI: 10.1021/acs.jctc.3c01224] [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: 11/04/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023]
Abstract
The process of drug design requires the initial identification of compounds that bind their targets with high affinity and selectivity. Advances in generative modeling of small molecules based on deep learning are offering novel opportunities for making this process faster and cheaper. Here, we propose an approach to achieve this goal, where predictions of binding affinity are used in conjunction with the Junction Tree Variational Autoencoder (JTVAE) whose latent space is used to facilitate the efficient exploration of the chemical space using a Bayesian optimization strategy. The exploration identifies small molecules predicted to have both high affinity and high selectivity by using an objective function that optimizes the binding to the target while penalizing the binding to off-targets. The framework is demonstrated for FMS-like tyrosine kinase 3 (FLT3) and shown to predict small molecules with predicted affinity and selectivity comparable to those of clinically approved drugs for this target.
Collapse
Affiliation(s)
- Raghav Chandra
- Centre for Misfolding Diseases,
Yusuf Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, U.K.
| | - Robert I. Horne
- Centre for Misfolding Diseases,
Yusuf Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, U.K.
| | - Michele Vendruscolo
- Centre for Misfolding Diseases,
Yusuf Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, U.K.
| |
Collapse
|
16
|
Qin Y, Li D, Qi C, Xiang H, Meng H, Liu J, Zhou S, Gong X, Li Y, Xu G, Zu R, Xie H, Xu Y, Xu G, Zhang Z, Chen S, Pan L, Li Y, Tan L. Structure-based development of potent and selective type-II kinase inhibitors of RIPK1. Acta Pharm Sin B 2024; 14:319-334. [PMID: 38261830 PMCID: PMC10793102 DOI: 10.1016/j.apsb.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 01/25/2024] Open
Abstract
Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) functions as a key regulator in inflammation and cell death and is involved in mediating a variety of inflammatory or degenerative diseases. A number of allosteric RIPK1 inhibitors (RIPK1i) have been developed, and some of them have already advanced into clinical evaluation. Recently, selective RIPK1i that interact with both the allosteric pocket and the ATP-binding site of RIPK1 have started to emerge. Here, we report the rational development of a new series of type-II RIPK1i based on the rediscovery of a reported but mechanistically atypical RIPK3i. We also describe the structure-guided lead optimization of a potent, selective, and orally bioavailable RIPK1i, 62, which exhibits extraordinary efficacies in mouse models of acute or chronic inflammatory diseases. Collectively, 62 provides a useful tool for evaluating RIPK1 in animal disease models and a promising lead for further drug development.
Collapse
Affiliation(s)
- Ying Qin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dekang Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chunting Qi
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
| | - Huaijiang Xiang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huyan Meng
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingli Liu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
| | - Shaoqing Zhou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinyu Gong
- University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Ying Li
- University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Guifang Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
| | - Rui Zu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
| | - Hang Xie
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yechun Xu
- University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Gang Xu
- Institute of Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518112, China
| | - Zheng Zhang
- Institute of Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518112, China
| | - Shi Chen
- Department of Burn and Plastic Surgery, Shenzhen Institute of Translational Medicine, Shenzhen University Medical School, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China
| | - Lifeng Pan
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Ying Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
| | - Li Tan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| |
Collapse
|
17
|
Liu JX, Na RS, Yang LJ, Huang XR, Zhao X. Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations. Phys Chem Chem Phys 2023; 25:31418-31430. [PMID: 37962373 DOI: 10.1039/d3cp03755j] [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: 11/15/2023]
Abstract
Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for the treatment of autoimmune, inflammatory, neurodegenerative, and ischemic diseases. So far, there are no approved RIPK1 inhibitors available. In this study, four machine learning algorithms were employed (random forest, extra trees, extreme gradient boosting and light gradient boosting machine) to predict small molecule inhibitors of RIPK1. The statistical metrics revealed similar performance and demonstrated outstanding predictive capabilities in all four models. Molecular docking and clustering analysis were employed to confirm six compounds that are structurally distinct from existing RIPK1 inhibitors. Subsequent molecular dynamics simulations were performed to evaluate the binding ability of these compounds. Utilizing the Shapley additive explanation (SHAP) method, the 1855 bit has been identified as the most significant molecular fingerprint fragment. The findings propose that these six small molecules exhibit promising potential for targeting RIPK1 in associated diseases. Notably, the identification of Cpd-1 small molecule (ZINC000085897746) from the Musa acuminate highlights its natural product origin, warranting further attention and investigation.
Collapse
Affiliation(s)
- Ji-Xiang Liu
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Liutiao Road #2, Changchun 130021, China.
| | - Ri-Song Na
- Collaborative Innovation Center of Henan Grain Crops, National Key Laboratory of Wheat and Maize Crop Science, College of Plant Protection, Henan Agricultural University, Zhengzhou 450002, China
| | - Lian-Juan Yang
- Department of Medical Mycology, Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai, 200443, China
| | - Xu-Ri Huang
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Liutiao Road #2, Changchun 130021, China.
| | - Xi Zhao
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Liutiao Road #2, Changchun 130021, China.
| |
Collapse
|
18
|
Xiong Y, Wang Y, Wang Y, Li C, Yusong P, Wu J, Wang Y, Gu L, Butch CJ. Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation. J Comput Aided Mol Des 2023; 37:507-517. [PMID: 37550462 DOI: 10.1007/s10822-023-00523-3] [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: 05/22/2023] [Accepted: 07/17/2023] [Indexed: 08/09/2023]
Abstract
Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10-20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.
Collapse
Affiliation(s)
- Youjin Xiong
- Department of Biomedical Engineering, Nanjing University, Nanjing, 210093, China
| | - Yiqing Wang
- Icekredit Incorporated, Shanghai, 200120, China
| | - Yisheng Wang
- Department of Biomedical Engineering, Nanjing University, Nanjing, 210093, China
| | - Chenmei Li
- Department of Biomedical Engineering, Nanjing University, Nanjing, 210093, China
| | - Peng Yusong
- Department of Biomedical Engineering, Nanjing University, Nanjing, 210093, China
| | - Junyu Wu
- Icekredit Incorporated, Shanghai, 200120, China
| | - Yiqing Wang
- Department of Biomedical Engineering, Nanjing University, Nanjing, 210093, China
| | - Lingyun Gu
- Department of Information Systems Technology and Design, Singapore University of Technology and Design, Singapore, Singapore.
| | - Christopher J Butch
- Department of Biomedical Engineering, Nanjing University, Nanjing, 210093, China.
| |
Collapse
|
19
|
Singh S, Kumar R, Payra S, Singh SK. Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery. Cureus 2023; 15:e44359. [PMID: 37779744 PMCID: PMC10539991 DOI: 10.7759/cureus.44359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2023] [Indexed: 10/03/2023] Open
Abstract
Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.
Collapse
Affiliation(s)
- Shruti Singh
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Rajesh Kumar
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Shuvasree Payra
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Sunil K Singh
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| |
Collapse
|
20
|
Ivanenkov Y, Zagribelnyy B, Malyshev A, Evteev S, Terentiev V, Kamya P, Bezrukov D, Aliper A, Ren F, Zhavoronkov A. The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry. ACS Med Chem Lett 2023; 14:901-915. [PMID: 37465301 PMCID: PMC10351082 DOI: 10.1021/acsmedchemlett.3c00041] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/09/2023] [Indexed: 07/20/2023] Open
Abstract
This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.
Collapse
Affiliation(s)
- Yan Ivanenkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Bogdan Zagribelnyy
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Alex Malyshev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Sergei Evteev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Victor Terentiev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Petrina Kamya
- Insilico
Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, Canada H3B 4W8
| | - Dmitry Bezrukov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Alex Aliper
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Feng Ren
- Insilico
Medicine Shanghai Ltd., Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong New District, Shanghai 201203, China
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| |
Collapse
|
21
|
Janet JP, Mervin L, Engkvist O. Artificial intelligence in molecular de novo design: Integration with experiment. Curr Opin Struct Biol 2023; 80:102575. [PMID: 36966692 DOI: 10.1016/j.sbi.2023.102575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/09/2023] [Accepted: 02/18/2023] [Indexed: 06/04/2023]
Abstract
In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progress and experimental validation of novel generative algorithms, the validation of QSAR models and how AI-based molecular de novo design is starting to become connected with chemistry automation. While progress has been made in the last few years, it is still early days. The experimental validations conducted thus far should be considered proof-of-principle, providing confidence that the field is moving in the right direction.
Collapse
Affiliation(s)
- Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| |
Collapse
|
22
|
Hu Z, Zhang Y, Yu W, Li J, Yao J, Zhang J, Wang J, Wang C. Transient receptor potential ankyrin 1 (TRPA1) modulators: Recent update and future perspective. Eur J Med Chem 2023; 257:115392. [PMID: 37269667 DOI: 10.1016/j.ejmech.2023.115392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 06/05/2023]
Abstract
The transient receptor potential ankyrin 1 (TRPA1) channel is a non-selective cation channel that senses irritant chemicals. Its activation is closely associated with pain, inflammation, and pruritus. TRPA1 antagonists are promising treatments for these diseases, and there has been a recent upsurge in their application to new areas such as cancer, asthma, and Alzheimer's disease. However, due to the generally disappointing performance of TRPA1 antagonists in clinical studies, scientists must pursue the development of antagonists with higher selectivity, metabolic stability, and solubility. Moreover, TRPA1 agonists provide a deeper understanding of activation mechanisms and aid in antagonist screening. Therefore, we summarize the TRPA1 antagonists and agonists developed in recent years, with a particular focus on structure-activity relationships (SARs) and pharmacological activity. In this perspective, we endeavor to keep abreast of cutting-edge ideas and provide inspiration for the development of more effective TRPA1-modulating drugs.
Collapse
Affiliation(s)
- Zelin Hu
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Ya Zhang
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Wenhan Yu
- College of Letters & Science, University of California, Berkeley, Berkeley, 94720, California, United States
| | - Junjie Li
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jiaqi Yao
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Jifa Zhang
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jiaxing Wang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, 38163, Tennessee, United States
| | - Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| |
Collapse
|