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Wang M, Wu Z, Wang J, Weng G, Kang Y, Pan P, Li D, Deng Y, Yao X, Bing Z, Hsieh CY, Hou T. Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space. J Chem Inf Model 2024; 64:1213-1228. [PMID: 38302422 DOI: 10.1021/acs.jcim.3c01964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.
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Affiliation(s)
- Mingyang Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Zhengjian Wu
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- School of Computer Science, Wuhan University, Wuhan 430072, Hubei ,China
| | - Jike Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Gaoqi Weng
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yu Kang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Peichen Pan
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Dan Li
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Xiaojun Yao
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery Macau Institute for Applied Research in Medicine and Health State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Zhitong Bing
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Tingjun Hou
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
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Simões T, Lopes D, Dias S, Fernandes F, Pereira J, Jorge J, Bajaj C, Gomes A. Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2017; 36:643-683. [PMID: 29520122 PMCID: PMC5839519 DOI: 10.1111/cgf.13158] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.
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Affiliation(s)
- Tiago Simões
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - Sérgio Dias
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - João Pereira
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | - Joaquim Jorge
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | | | - Abel Gomes
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
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3
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Pirard B. The quest for novel chemical matter and the contribution of computer-aided de novo design. Expert Opin Drug Discov 2012; 6:225-31. [PMID: 22647201 DOI: 10.1517/17460441.2011.554394] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Identifying novel chemical matter is the focus of many drug discovery efforts. Through these efforts, computer-based de novo design of drug-like molecules, which aim to build an entire molecule 'from scratch', has emerged as a valuable approach to identify novel chemical matter. In this paper, the author discusses the recent research efforts that aim to build, in silico, more chemically accessible molecules, sample more efficiently the chemical space and rank the proposed molecules. The author reviews de novo design algorithms developed between 2008 and 2010 and the issue of validation, and highlights some recent successful applications of de novo design to drug discovery projects. Although research has addressed the lack of synthetic accessibility of the molecules proposed by the first generation of de novo design tools, the lack of accurate scoring function remains a major limitation of structure-based de novo design. However, de novo design is a valuable approach to generate either chemical starting points or ideas.
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Affiliation(s)
- Bernard Pirard
- Novartis Institute for BioMedical Research, Global Discovery Chemistry, Computer-Aided Drug Discovery, Forum 1, CH-4002 Basel, Switzerland +41 61 32 45 620 ;
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4
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Addicoat MA, Page AJ, Brain ZE, Flack L, Morokuma K, Irle S. Optimization of a Genetic Algorithm for the Functionalization of Fullerenes. J Chem Theory Comput 2012; 8:1841-51. [DOI: 10.1021/ct300190u] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Matthew A. Addicoat
- Department
of Computer Science, Australian National University, ACT, 0200, Australia
- Department
of Chemistry, Graduate School of Science, Nagoya University, Nagoya 464-4602, Japan
| | - Alister J. Page
- Fukui Institute for Fundamental
Chemistry, Kyoto University, Kyoto 606-8103,
Japan
| | - Zoe E. Brain
- Department
of Computer Science, Australian National University, ACT, 0200, Australia
| | - Lloyd Flack
- Department
of Rheumatology, University of New South Wales, NSW, 2052, Australia
| | - Keiji Morokuma
- Fukui Institute for Fundamental
Chemistry, Kyoto University, Kyoto 606-8103,
Japan
- Cherry L. Emerson Center for
Scientic Computation and Department of Chemistry, Emory University, Atlanta, Georgia 30322,
United States
| | - Stephan Irle
- Department
of Chemistry, Graduate School of Science, Nagoya University, Nagoya 464-4602, Japan
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Hartenfeller M, Schneider G. Enabling future drug discovery by
de novo
design. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2011. [DOI: 10.1002/wcms.49] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Markus Hartenfeller
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
| | - Gisbert Schneider
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
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