1
|
Muegge I, Bentzien J, Ge Y. Perspectives on current approaches to virtual screening in drug discovery. Expert Opin Drug Discov 2024:1-11. [PMID: 39132881 DOI: 10.1080/17460441.2024.2390511] [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: 06/29/2024] [Accepted: 08/06/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods. AREAS COVERED The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS. EXPERT OPINION The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.
Collapse
Affiliation(s)
- Ingo Muegge
- Research department, Alkermes, Inc, Waltham, MA, USA
| | - Jörg Bentzien
- Research department, Alkermes, Inc, Waltham, MA, USA
| | - Yunhui Ge
- Research department, Alkermes, Inc, Waltham, MA, USA
| |
Collapse
|
2
|
Wu JN, Wang T, Chen Y, Tang LJ, Wu HL, Yu RQ. t-SMILES: a fragment-based molecular representation framework for de novo ligand design. Nat Commun 2024; 15:4993. [PMID: 38862578 PMCID: PMC11167009 DOI: 10.1038/s41467-024-49388-6] [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: 07/28/2022] [Accepted: 06/04/2024] [Indexed: 06/13/2024] Open
Abstract
Effective representation of molecules is a crucial factor affecting the performance of artificial intelligence models. This study introduces a flexible, fragment-based, multiscale molecular representation framework called t-SMILES (tree-based SMILES) with three code algorithms: TSSA (t-SMILES with shared atom), TSDY (t-SMILES with dummy atom but without ID) and TSID (t-SMILES with ID and dummy atom). It describes molecules using SMILES-type strings obtained by performing a breadth-first search on a full binary tree formed from a fragmented molecular graph. Systematic evaluations using JTVAE, BRICS, MMPA, and Scaffold show the feasibility of constructing a multi-code molecular description system, where various descriptions complement each other, enhancing the overall performance. In addition, it can avoid overfitting and achieve higher novelty scores while maintaining reasonable similarity on labeled low-resource datasets, regardless of whether the model is original, data-augmented, or pre-trained then fine-tuned. Furthermore, it significantly outperforms classical SMILES, DeepSMILES, SELFIES and baseline models in goal-directed tasks. And it surpasses state-of-the-art fragment, graph and SMILES based approaches on ChEMBL, Zinc, and QM9.
Collapse
Affiliation(s)
- Juan-Ni Wu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China
| | - Tong Wang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China
| | - Yue Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China
| | - Li-Juan Tang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China
| | - Hai-Long Wu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China.
| | - Ru-Qin Yu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China.
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Bess A, Berglind F, Mukhopadhyay S, Brylinski M, Alvin C, Fattah F, Wasan KM. Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2. Front Pharmacol 2023; 14:1297924. [PMID: 38186640 PMCID: PMC10770831 DOI: 10.3389/fphar.2023.1297924] [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] [Received: 09/29/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose: This study introduces a sophisticated computational pipeline, eVir, designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g., COVID-19), particularly in resource-limited countries. Therefore, our team devised an Artificial Intelligence (AI) system to explore repurposing opportunities for currently used oral therapies. The eVir system operates by identifying pharmaceutical compounds that mirror the effects of antiviral peptides (AVPs)-fragments of human proteins known to interfere with fundamental phases of the viral life cycle: entry, fusion, and replication. eVir extrapolates the probable antiviral efficacy of a given compound by analyzing its established and predicted impacts on the human protein-protein interaction network. This innovative approach provides a promising platform for drug repurposing against SARS-CoV-2 or any virus for which peptide data is available. Methods: The eVir AI software pipeline processes drug-protein and protein-protein interaction networks generated from open-source datasets. eVir uses Node2Vec, a graph embedding technique, to understand the nuanced connections among drugs and proteins. The embeddings are input a Siamese Network (SNet) and MLPs, each tailored for the specific mechanisms of entry, fusion, and replication, to evaluate the similarity between drugs and AVPs. Scores generated from the SNet and MLPs undergo a Platt probability calibration and are combined into a unified score that gauges the potential antiviral efficacy of a drug. This integrated approach seeks to boost drug identification confidence, offering a potential solution for detecting therapeutic candidates with pronounced antiviral potency. Once identified a number of compounds were tested for efficacy and toxicity in lung carcinoma cells (Calu-3) infected with SARS-CoV-2. A lead compound was further identified to determine its efficacy and toxicity in K18-hACE2 mice infected with SARS-CoV-2. Computational Predictions: The SNet confidently differentiated between similar and dissimilar drug pairs with an accuracy of 97.28% and AUC of 99.47%. Key compounds identified through these networks included Zinc, Mebendazole, Levomenol, Gefitinib, Niclosamide, and Imatinib. Notably, Mebendazole and Zinc showcased the highest similarity scores, while Imatinib, Levemenol, and Gefitinib also ranked within the top 20, suggesting their significant pharmacological potentials. Further examination of protein binding analysis using explainable AI focused on reverse engineering the causality of the networks. Protein interaction scores for Mebendazole and Imatinib revealed their effects on notable proteins such as CDPK1, VEGF2, ABL1, and several tyrosine protein kinases. Laboratory Studies: This study determined that Mebendazole, Gefitinib, Topotecan and to some extent Carfilzomib showed conventional drug-response curves, with IC50 values near or below that of Remdesivir with excellent confidence all above R2>0.91, and no cytotoxicity at the IC50 concentration in Calu-3 cells. Cyclosporine A showed antiviral activity, but also unconventional drug-response curves and low R2 which are explained by the non-dose dependent toxicity of the compound. Additionally, Niclosamide demonstrated a conventional drug-response curve with high confidence; however, its inherent cytotoxicity may be a confounding element that misrepresents true antiviral efficacy, by reflecting cellular damage rather than a genuine antiviral action. Remdesivir was used as a control compound and was evaluated in parallel with the submitted test article and had conventional drug-response curves validating the overall results of the assay. Mebendazole was identified from the cell studies to have efficacy at non-toxic concentrations and were further evaluated in mice infected with SARS-CoV-2. Mebendazole administered to K18-hACE2 mice infected with SARS-CoV-2, resulted in a 44.2% reduction in lung viral load compared to non-treated placebo control respectively. There were no significant differences in body weight and all clinical chemistry determinations evaluated (i.e., kidney and liver enzymes) between the different treatment groups. Conclusion: This research underscores the potential of repurposing existing compounds for treating COVID-19. Our preliminary findings underscore the therapeutic promise of several compounds, notably Mebendazole, in both in vitro and in vivo settings against SARS-CoV-2. Several of the drugs explored, especially Mebendazole, are off-label medication; their cost-effectiveness position them as economical therapies against SARS-CoV-2.
Collapse
Affiliation(s)
- Adam Bess
- Department of Computer Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Frej Berglind
- Department of Computer Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Supratik Mukhopadhyay
- Department of Environmental Sciences, Center for Computation & Technology, Coastal Studies Institute, Louisiana State University, Baton Rouge, LA, United States
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Chris Alvin
- Department of Computer Science, Furman University, Greenville, SC, United States
| | - Fanan Fattah
- Department of Urologic Sciences, Faculty of Medicine and the Neglected Global Diseases Initiative, University of British Columbia, Vancouver, BC, Canada
| | - Kishor M. Wasan
- Department of Urologic Sciences, Faculty of Medicine and the Neglected Global Diseases Initiative, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
5
|
Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite. Mol Divers 2022:10.1007/s11030-022-10506-5. [PMID: 35925528 PMCID: PMC9362107 DOI: 10.1007/s11030-022-10506-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 07/23/2022] [Indexed: 11/04/2022]
Abstract
A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085.
Collapse
|
6
|
Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases. Drug Discov Today 2021; 27:1099-1107. [PMID: 34748992 PMCID: PMC8570449 DOI: 10.1016/j.drudis.2021.10.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/26/2021] [Accepted: 10/29/2021] [Indexed: 01/08/2023]
Abstract
The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences.
Collapse
|
7
|
Substituted 1-methyl-4-phenylpyrrolidin-2-ones – Fragment-based design of N-methylpyrrolidone-derived bromodomain inhibitors. Eur J Med Chem 2020; 191:112120. [DOI: 10.1016/j.ejmech.2020.112120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 01/16/2020] [Accepted: 02/03/2020] [Indexed: 01/12/2023]
|
8
|
Wang Z, Sun H, Shen C, Hu X, Gao J, Li D, Cao D, Hou T. Combined strategies in structure-based virtual screening. Phys Chem Chem Phys 2020; 22:3149-3159. [PMID: 31995074 DOI: 10.1039/c9cp06303j] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.
Collapse
Affiliation(s)
- Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Xueping Hu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Junbo Gao
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Dan Li
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, P. R. China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| |
Collapse
|
9
|
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol 2019; 20:2. [PMID: 30621790 PMCID: PMC6325674 DOI: 10.1186/s40360-018-0282-6] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 12/26/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery. RESULTS In this work, we describe eToxPred, a new approach to reliably estimate the toxicity and synthetic accessibility of small organic compounds. eToxPred employs machine learning algorithms trained on molecular fingerprints to evaluate drug candidates. The performance is assessed against multiple datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds. Encouragingly, eToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%. CONCLUSIONS eToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred .
Collapse
Affiliation(s)
- Limeng Pu
- Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Tairan Liu
- Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Hsiao-Chun Wu
- Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Supratik Mukhopadhyay
- Department of Computer Science, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA. .,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
| |
Collapse
|
10
|
Abstract
Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .
Collapse
Affiliation(s)
- Junmei Wang
- Department of Pharmaceutical Sciences , The University of Pittsburgh , 3501 Terrace Street , Pittsburgh , Pennsylvania 15261 , United States
| | - Yubin Ge
- Department of Pharmaceutical Sciences , The University of Pittsburgh , 3501 Terrace Street , Pittsburgh , Pennsylvania 15261 , United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences , The University of Pittsburgh , 3501 Terrace Street , Pittsburgh , Pennsylvania 15261 , United States
| |
Collapse
|
11
|
Bian Y, Xie XQS. Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications. AAPS JOURNAL 2018; 20:59. [PMID: 29633051 DOI: 10.1208/s12248-018-0216-7] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 03/08/2018] [Indexed: 01/08/2023]
Abstract
Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.
Collapse
Affiliation(s)
- Yuemin Bian
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.,NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA
| | - Xiang-Qun Sean Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA. .,NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA. .,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA. .,Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA. .,Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.
| |
Collapse
|
12
|
Batiste L, Unzue A, Dolbois A, Hassler F, Wang X, Deerain N, Zhu J, Spiliotopoulos D, Nevado C, Caflisch A. Chemical Space Expansion of Bromodomain Ligands Guided by in Silico Virtual Couplings (AutoCouple). ACS CENTRAL SCIENCE 2018; 4:180-188. [PMID: 29532017 PMCID: PMC5833004 DOI: 10.1021/acscentsci.7b00401] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Indexed: 10/24/2023]
Abstract
Expanding the chemical space and simultaneously ensuring synthetic accessibility is of upmost importance, not only for the discovery of effective binders for novel protein classes but, more importantly, for the development of compounds against hard-to-drug proteins. Here, we present AutoCouple, a de novo approach to computational ligand design focused on the diversity-oriented generation of chemical entities via virtual couplings. In a benchmark application, chemically diverse compounds with low-nanomolar potency for the CBP bromodomain and high selectivity against the BRD4(1) bromodomain were achieved by the synthesis of about 50 derivatives of the original fragment. The binding mode was confirmed by X-ray crystallography, target engagement in cells was demonstrated, and antiproliferative activity was showcased in three cancer cell lines. These results reveal AutoCouple as a useful in silico coupling method to expand the chemical space in hit optimization campaigns resulting in potent, selective, and cell permeable bromodomain ligands.
Collapse
Affiliation(s)
- Laurent Batiste
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Andrea Unzue
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Aymeric Dolbois
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Fabrice Hassler
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Xuan Wang
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Nicholas Deerain
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Jian Zhu
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Dimitrios Spiliotopoulos
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Cristina Nevado
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Amedeo Caflisch
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| |
Collapse
|
13
|
Berenger F, Vu O, Meiler J. Consensus queries in ligand-based virtual screening experiments. J Cheminform 2017; 9:60. [PMID: 29185065 PMCID: PMC5705545 DOI: 10.1186/s13321-017-0248-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 11/20/2017] [Indexed: 11/10/2022] Open
Abstract
Background In ligand-based virtual screening experiments, a known active ligand is used in similarity searches to find putative active compounds for the same protein target. When there are several known active molecules, screening using all of them is more powerful than screening using a single ligand. A consensus query can be created by either screening serially with different ligands before merging the obtained similarity scores, or by combining the molecular descriptors (i.e. chemical fingerprints) of those ligands. Results We report on the discriminative power and speed of several consensus methods, on two datasets only made of experimentally verified molecules. The two datasets contain a total of 19 protein targets, 3776 known active and ~ 2 × 106 inactive molecules. Three chemical fingerprints are investigated: MACCS 166 bits, ECFP4 2048 bits and an unfolded version of MOLPRINT2D. Four different consensus policies and five consensus sizes were benchmarked. Conclusions The best consensus method is to rank candidate molecules using the maximum score obtained by each candidate molecule versus all known actives. When the number of actives used is small, the same screening performance can be approached by a consensus fingerprint. However, if the computational exploration of the chemical space is limited by speed (i.e. throughput), a consensus fingerprint allows to outperform this consensus of scores.
Collapse
Affiliation(s)
- Francois Berenger
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA. .,Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
| | - Oanh Vu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
14
|
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag. J Chem Inf Model 2017; 57:627-631. [PMID: 28346786 PMCID: PMC5433162 DOI: 10.1021/acs.jcim.6b00596] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
![]()
Constructing high-quality
libraries of molecular building blocks
is essential for successful fragment-based drug discovery. In this
communication, we describe eMolFrag, a new open-source
software to decompose organic compounds into nonredundant fragments
retaining molecular connectivity information. Given a collection of
molecules, eMolFrag generates a set of unique fragments
comprising larger moieties, bricks, and smaller linkers connecting
bricks. These building blocks can subsequently be used to construct
virtual screening libraries for targeted drug discovery. The robustness
and computational performance of eMolFrag is assessed
against the Directory of Useful Decoys, Enhanced database conducted
in serial and parallel modes with up to 16 computing cores. Further,
the application of eMolFrag in de novo drug design
is illustrated using the adenosine receptor. eMolFrag
is implemented in Python, and it is available as stand-alone software
and a web server at www.brylinski.org/emolfrag and https://github.com/liutairan/eMolFrag.
Collapse
Affiliation(s)
| | | | - Chris Alvin
- Department of Computer Science and Information Systems, Bradley University , Peoria, Illinois 61625, United States
| | | | | |
Collapse
|
15
|
Abstract
Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.
Collapse
Affiliation(s)
- Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400 Illkirch, France.
| |
Collapse
|