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Zhang H, Liu X, Cheng W, Wang T, Chen Y. Prediction of drug-target binding affinity based on deep learning models. Comput Biol Med 2024; 174:108435. [PMID: 38608327 DOI: 10.1016/j.compbiomed.2024.108435] [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: 01/29/2024] [Revised: 04/05/2024] [Accepted: 04/07/2024] [Indexed: 04/14/2024]
Abstract
The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.
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Affiliation(s)
- Hao Zhang
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiaoqian Liu
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wenya Cheng
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Tianshi Wang
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yuanyuan Chen
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China.
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2
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Thomas JR, Shelton C, Murphy J, Brittain S, Bray MA, Aspesi P, Concannon J, King FJ, Ihry RJ, Ho DJ, Henault M, Hadjikyriacou A, Neri M, Sigoillot FD, Pham HT, Shum M, Barys L, Jones MD, Martin EJ, Blechschmidt A, Rieffel S, Troxler TJ, Mapa FA, Jenkins JL, Jain RK, Kutchukian PS, Schirle M, Renner S. Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles. ACS Chem Biol 2024; 19:938-952. [PMID: 38565185 PMCID: PMC11040606 DOI: 10.1021/acschembio.3c00737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 04/04/2024]
Abstract
Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.
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Affiliation(s)
- Jason R. Thomas
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Claude Shelton
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Jason Murphy
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Scott Brittain
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Mark-Anthony Bray
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Peter Aspesi
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - John Concannon
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Frederick J. King
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Robert J. Ihry
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Daniel J. Ho
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Martin Henault
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | | | - Marilisa Neri
- Novartis
Biomedical Research, Basel 4056, Switzerland
| | | | - Helen T. Pham
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Matthew Shum
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Louise Barys
- Novartis
Biomedical Research, Basel 4056, Switzerland
| | - Michael D. Jones
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Eric J. Martin
- Novartis
Biomedical Research, Emeryville, California 94608, United States
| | | | | | | | - Felipa A. Mapa
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Jeremy L. Jenkins
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Rishi K. Jain
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | | | - Markus Schirle
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
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Shen X, Zeng T, Chen N, Li J, Wu R. NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer. Molecules 2024; 29:1867. [PMID: 38675687 PMCID: PMC11053988 DOI: 10.3390/molecules29081867] [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: 03/05/2024] [Revised: 04/11/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024] Open
Abstract
Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure-property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.
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Affiliation(s)
- Xiaojuan Shen
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China; (X.S.); (T.Z.); (N.C.)
| | - Tao Zeng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China; (X.S.); (T.Z.); (N.C.)
| | - Nianhang Chen
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China; (X.S.); (T.Z.); (N.C.)
| | - Jiabo Li
- ChemXAI Inc., 53 Barry Lane, Syosset, NY 11791, USA
| | - Ruibo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China; (X.S.); (T.Z.); (N.C.)
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4
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Shen T, Li S, Wang XS, Wang D, Wu S, Xia J, Zhang L. Deep reinforcement learning enables better bias control in benchmark for virtual screening. Comput Biol Med 2024; 171:108165. [PMID: 38402838 DOI: 10.1016/j.compbiomed.2024.108165] [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: 11/01/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/27/2024]
Abstract
Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.
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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
| | - Shan Li
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, USA
| | - 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.
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
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5
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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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Weng G, Zhao H, Nie D, Zhang H, Liu L, Hou T, Kang Y. RediscMol: Benchmarking Molecular Generation Models in Biological Properties. J Med Chem 2024; 67:1533-1543. [PMID: 38181194 DOI: 10.1021/acs.jmedchem.3c02051] [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: 01/07/2024]
Abstract
Deep learning-based molecular generative models have garnered emerging attention for their capability to generate molecules with novel structures and desired physicochemical properties. However, the evaluation of these models, particularly in a biological context, remains insufficient. To address the limitations of existing metrics and emulate practical application scenarios, we construct the RediscMol benchmark that comprises active molecules extracted from 5 kinase and 3 GPCR data sets. A set of rediscovery- and similarity-related metrics are introduced to assess the performance of 8 representative generative models (CharRNN, VAE, Reinvent, AAE, ORGAN, RNNAttn, TransVAE, and GraphAF). Our findings based on the RediscMol benchmark differ from those of previous evaluations. CharRNN, VAE, and Reinvent exhibit a greater ability to reproduce known active molecules, while RNNAttn, TransVAE, and GraphAF struggle in this aspect despite their notable performance on commonly used distribution-learning metrics. Our evaluation framework may provide valuable guidance for advancing generative models in real-world drug design scenarios.
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Affiliation(s)
- Gaoqi Weng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Huifeng Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Dou Nie
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen 518129, Guangdong, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
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7
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Siqueira-Neto JL, Wicht KJ, Chibale K, Burrows JN, Fidock DA, Winzeler EA. Antimalarial drug discovery: progress and approaches. Nat Rev Drug Discov 2023; 22:807-826. [PMID: 37652975 PMCID: PMC10543600 DOI: 10.1038/s41573-023-00772-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/17/2023] [Indexed: 09/02/2023]
Abstract
Recent antimalarial drug discovery has been a race to produce new medicines that overcome emerging drug resistance, whilst considering safety and improving dosing convenience. Discovery efforts have yielded a variety of new molecules, many with novel modes of action, and the most advanced are in late-stage clinical development. These discoveries have led to a deeper understanding of how antimalarial drugs act, the identification of a new generation of drug targets, and multiple structure-based chemistry initiatives. The limited pool of funding means it is vital to prioritize new drug candidates. They should exhibit high potency, a low propensity for resistance, a pharmacokinetic profile that favours infrequent dosing, low cost, preclinical results that demonstrate safety and tolerability in women and infants, and preferably the ability to block Plasmodium transmission to Anopheles mosquito vectors. In this Review, we describe the approaches that have been successful, progress in preclinical and clinical development, and existing challenges. We illustrate how antimalarial drug discovery can serve as a model for drug discovery in diseases of poverty.
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Affiliation(s)
| | - Kathryn J Wicht
- Holistic Drug Discovery and Development (H3D) Centre, University of Cape Town, Rondebosch, South Africa
- South African Medical Research Council Drug Discovery and Development Research Unit, Department of Chemistry and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch, South Africa
| | - Kelly Chibale
- Holistic Drug Discovery and Development (H3D) Centre, University of Cape Town, Rondebosch, South Africa
- South African Medical Research Council Drug Discovery and Development Research Unit, Department of Chemistry and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch, South Africa
| | | | - David A Fidock
- Department of Microbiology and Immunology and Center for Malaria Therapeutics and Antimicrobial Resistance, Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
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Smer-Barreto V, Quintanilla A, Elliott RJR, Dawson JC, Sun J, Campa VM, Lorente-Macías Á, Unciti-Broceta A, Carragher NO, Acosta JC, Oyarzún DA. Discovery of senolytics using machine learning. Nat Commun 2023; 14:3445. [PMID: 37301862 PMCID: PMC10257182 DOI: 10.1038/s41467-023-39120-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
Cellular senescence is a stress response involved in ageing and diverse disease processes including cancer, type-2 diabetes, osteoarthritis and viral infection. Despite growing interest in targeted elimination of senescent cells, only few senolytics are known due to the lack of well-characterised molecular targets. Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various modalities of senescence. The compounds have potency comparable to known senolytics, and we show that oleandrin has improved potency over its target as compared to best-in-class alternatives. Our approach led to several hundred-fold reduction in drug screening costs and demonstrates that artificial intelligence can take maximum advantage of small and heterogeneous drug screening data, paving the way for new open science approaches to early-stage drug discovery.
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Affiliation(s)
- Vanessa Smer-Barreto
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK.
| | - Andrea Quintanilla
- Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain
| | - Richard J R Elliott
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Jiugeng Sun
- School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK
| | - Víctor M Campa
- Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain
| | - Álvaro Lorente-Macías
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Asier Unciti-Broceta
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Juan Carlos Acosta
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK.
- Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain.
| | - Diego A Oyarzún
- School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK.
- School of Biological Sciences, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK.
- The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK.
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Chen L, Yu L, Gao L. Potent antibiotic design via guided search from antibacterial activity evaluations. Bioinformatics 2023; 39:7008322. [PMID: 36707990 PMCID: PMC9897189 DOI: 10.1093/bioinformatics/btad059] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/14/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. RESULTS By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. AVAILABILITY AND IMPLEMENTATION Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lu Chen
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
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Chan L, Kumar R, Verdonk M, Poelking C. A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00564-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Mughal H, Bell EC, Mughal K, Derbyshire ER, Freundlich JS. Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents. ACS Infect Dis 2022; 8:1553-1562. [PMID: 35894649 PMCID: PMC9987178 DOI: 10.1021/acsinfecdis.2c00189] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The need for novel antimalarials is apparent given the continuing disease burden worldwide, despite significant drug discovery advances from the bench to the bedside. In particular, small-molecule agents with potent efficacy against both the liver and blood stages of Plasmodium parasite infection are critical for clinical settings as they would simultaneously prevent and treat malaria with a reduced selection pressure for resistance. While experimental screens for such dual-stage inhibitors have been conducted, the time and cost of these efforts limit their scope. Here, we have focused on leveraging machine learning approaches to discover novel antimalarials with such properties. A random forest modeling approach was taken to predict small molecules with in vitro efficacy versus liver-stage Plasmodium berghei parasites and a lack of human liver cell cytotoxicity. Empirical validation of the model was achieved with the realization of hits with liver-stage efficacy after prospective scoring of a commercial diversity library and consideration of structural diversity. A subset of these hits also demonstrated promising blood-stage Plasmodium falciparum efficacy. These 18 validated dual-stage antimalarials represent novel starting points for drug discovery and mechanism of action studies with significant potential for seeding a new generation of therapies.
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Affiliation(s)
- Haseeb Mughal
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University – New Jersey Medical School, 185 South Orange Ave, Newark, NJ, 07103
| | - Elise C. Bell
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, USA
| | - Khadija Mughal
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University – New Jersey Medical School, 185 South Orange Ave, Newark, NJ, 07103
| | - Emily R. Derbyshire
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, USA
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, 213 Research Drive, Durham, NC 27710, USA
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University – New Jersey Medical School, 185 South Orange Ave, Newark, NJ, 07103
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University – New Jersey Medical School, Newark, NJ, 07103
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Li S, Wang L, Meng J, Zhao Q, Zhang L, Liu H. De Novo design of potential inhibitors against SARS-CoV-2 Mpro. Comput Biol Med 2022; 147:105728. [PMID: 35763931 PMCID: PMC9197785 DOI: 10.1016/j.compbiomed.2022.105728] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/31/2022] [Accepted: 06/11/2022] [Indexed: 11/16/2022]
Abstract
The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. The validity (0.9248), novelty (0.9668), and uniqueness (0.0652) of the designed potential MproI library (PMproIL) were evaluated, and the results showed that MproI-GEN could be used to design structurally novel and reasonable molecules. Additionally, PMproIL was filtered based on machine learning models and molecular docking. After filtering, the potential MproIs were verified with molecular dynamics simulations to evaluate the binding stability levels of these MproIs and SARS-CoV-2 Mpro, thereby illustrating the inhibitory effects of the potential MproIs against Mpro. Two potential MproIs were proposed in this study. This study provides not only new possibilities for the development of COVID-19 drugs but also a complete pipeline for the discovery of novel lead compounds.
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Affiliation(s)
- Shimeng Li
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Lianxin Wang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Jinhui Meng
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China; Shenyang Key Laboratory of Computer Simulating and Information Processing of Bio-macromolecules, Shenyang, 110036, China.
| | - Hongsheng Liu
- Shenyang Key Laboratory of Computer Simulating and Information Processing of Bio-macromolecules, Shenyang, 110036, China; Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China; School of Pharmaceutical Sciences, Liaoning University, Shenyang, 110036, China.
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Abstract
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One application area
of computational methods in drug discovery
is the automated design of small molecules. Despite the large number
of publications describing methods and their application in both retrospective
and prospective studies, there is a lack of agreement on terminology
and key attributes to distinguish these various systems. We introduce
Automated Chemical Design (ACD) Levels to clearly define the level
of autonomy along the axes of ideation and decision making. To fully
illustrate this framework, we provide literature exemplars and place
some notable methods and applications into the levels. The ACD framework
provides a common language for describing automated small molecule
design systems and enables medicinal chemists to better understand
and evaluate such systems.
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Affiliation(s)
- Brian Goldman
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Steven Kearnes
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Trevor Kramer
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Patrick Riley
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - W Patrick Walters
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
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Potent antimalarial drugs with validated activities. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00451-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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