1
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Atz K, Cotos L, Isert C, Håkansson M, Focht D, Hilleke M, Nippa DF, Iff M, Ledergerber J, Schiebroek CCG, Romeo V, Hiss JA, Merk D, Schneider P, Kuhn B, Grether U, Schneider G. Prospective de novo drug design with deep interactome learning. Nat Commun 2024; 15:3408. [PMID: 38649351 PMCID: PMC11035696 DOI: 10.1038/s41467-024-47613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Maria Håkansson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Dorota Focht
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Mattis Hilleke
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Michael Iff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Jann Ledergerber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Carl C G Schiebroek
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Valentina Romeo
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Jan A Hiss
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Petra Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Bernd Kuhn
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
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2
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Qiu Y, Cheng F. Artificial intelligence for drug discovery and development in Alzheimer's disease. Curr Opin Struct Biol 2024; 85:102776. [PMID: 38335558 DOI: 10.1016/j.sbi.2024.102776] [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: 10/25/2023] [Revised: 12/29/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024]
Abstract
The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.
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Affiliation(s)
- Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA. https://twitter.com/YunguangQiu
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
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3
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Tang Y, Moretti R, Meiler J. Recent Advances in Automated Structure-Based De Novo Drug Design. J Chem Inf Model 2024; 64:1794-1805. [PMID: 38485516 DOI: 10.1021/acs.jcim.4c00247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.
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Affiliation(s)
- Yidan Tang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
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4
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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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5
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Colliandre L, Muller C. Bayesian Optimization in Drug Discovery. Methods Mol Biol 2024; 2716:101-136. [PMID: 37702937 DOI: 10.1007/978-1-0716-3449-3_5] [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: 09/14/2023]
Abstract
Drug discovery deals with the search for initial hits and their optimization toward a targeted clinical profile. Throughout the discovery pipeline, the candidate profile will evolve, but the optimization will mainly stay a trial-and-error approach. Tons of in silico methods have been developed to improve and fasten this pipeline. Bayesian optimization (BO) is a well-known method for the determination of the global optimum of a function. In the last decade, BO has gained popularity in the early drug design phase. This chapter starts with the concept of black box optimization applied to drug design and presents some approaches to tackle it. Then it focuses on BO and explains its principle and all the algorithmic building blocks needed to implement it. This explanation aims to be accessible to people involved in drug discovery projects. A strong emphasis is made on the solutions to deal with the specific constraints of drug discovery. Finally, a large set of practical applications of BO is highlighted.
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6
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Qin R, Zhang H, Huang W, Shao Z, Lei J. Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A 2B receptor antagonists. J Biomol Struct Dyn 2023:1-17. [PMID: 38133953 DOI: 10.1080/07391102.2023.2295974] [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: 09/16/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
The Adenosine A2B receptor (A2BAR) is considered a novel potential target for the immunotherapy of cancer, and A2BAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of benzimidazole-pyrazine scaffolds whose derivatives exhibited the antagonistic effect but lacked subtype selectivity towards A2BAR. In this work, we developed a scaffold-based protocol that incorporates a deep generative model and multilayer virtual screening to design benzimidazole-pyrazine derivatives as potential selective A2BAR antagonists. By utilizing a generative model with reported A2BAR antagonists as the training set, we built up a scaffold-focused library of benzimidazole-pyrazine derivatives and processed a virtual screening protocol to discover potential A2BAR antagonists. Finally, five molecules with different Bemis-Murcko scaffolds were identified and exhibited higher binding free energies than the reference molecule 12o. Further computational analysis revealed that the 3-benzyl derivative ABA-1266 presented high selectivity toward A2BAR and showed preferred draggability, providing future potent development of selective A2BAR antagonists.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rui Qin
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Hao Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Weifeng Huang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Zhenglin Shao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Jinping Lei
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
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7
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Du H, Jiang D, Zhang O, Wu Z, Gao J, Zhang X, Wang X, Deng Y, Kang Y, Li D, Pan P, Hsieh CY, Hou T. A flexible data-free framework for structure-based de novo drug design with reinforcement learning. Chem Sci 2023; 14:12166-12181. [PMID: 37969589 PMCID: PMC10631243 DOI: 10.1039/d3sc04091g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 10/11/2023] [Indexed: 11/17/2023] Open
Abstract
Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.
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Affiliation(s)
- Hongyan Du
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dejun Jiang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Odin Zhang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Zhenxing Wu
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Junbo Gao
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xujun Zhang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiaorui Wang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology Macao 999078 China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
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8
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Zhu H, Zhou R, Cao D, Tang J, Li M. A pharmacophore-guided deep learning approach for bioactive molecular generation. Nat Commun 2023; 14:6234. [PMID: 37803000 PMCID: PMC10558534 DOI: 10.1038/s41467-023-41454-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/30/2023] [Indexed: 10/08/2023] Open
Abstract
The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.
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Affiliation(s)
- Huimin Zhu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Renyi Zhou
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
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9
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Zhang O, Wang T, Weng G, Jiang D, Wang N, Wang X, Zhao H, Wu J, Wang E, Chen G, Deng Y, Pan P, Kang Y, Hsieh CY, Hou T. Learning on topological surface and geometric structure for 3D molecular generation. NATURE COMPUTATIONAL SCIENCE 2023; 3:849-859. [PMID: 38177756 DOI: 10.1038/s43588-023-00530-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/06/2023] [Indexed: 01/06/2024]
Abstract
Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes. The omission of these fine details leads to many models having difficulty in outputting reasonable molecules for a variety of therapeutic targets. Here, to address this challenge, we formulate a model, called SurfGen, that designs molecules in a fashion closely resembling the figurative key-and-lock principle. SurfGen comprises two equivariant neural networks, Geodesic-GNN and Geoatom-GNN, which capture the topological interactions on the pocket surface and the spatial interaction between ligand atoms and surface nodes, respectively. SurfGen outperforms other methods in a number of benchmarks, and its high sensitivity on the pocket structures enables an effective generative-model-based solution to the thorny issue of mutation-induced drug resistance.
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Affiliation(s)
- Odin Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Tianyue Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Gaoqi Weng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ning Wang
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, China
| | - Xiaorui Wang
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, China
| | - Huifeng Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jialu Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ercheng Wang
- Zhejiang Lab, Zhejiang University, Hangzhou, China
| | | | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
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10
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Wei L, Xu M, Liu Z, Jiang C, Lin X, Hu Y, Wen X, Zou R, Peng C, Lin H, Wang G, Yang L, Fang L, Yang M, Zhang P. Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study. J Chem Inf Model 2023; 63:5341-5355. [PMID: 37549337 DOI: 10.1021/acs.jcim.3c00543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.
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Affiliation(s)
- Lin Wei
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Min Xu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Zhiqiang Liu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Chongguo Jiang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Xiaohua Lin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Yaogang Hu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Xiaoming Wen
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Rongfeng Zou
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Chunwang Peng
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Hongrui Lin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Guo Wang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Lijun Yang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Lei Fang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Mingjun Yang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
| | - Peiyu Zhang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Shenzhen 518000, China
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11
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Jin J, Wang D, Shi G, Bao J, Wang J, Zhang H, Pan P, Li D, Yao X, Liu H, Hou T, Kang Y. FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization. J Med Chem 2023; 66:10808-10823. [PMID: 37471134 DOI: 10.1021/acs.jmedchem.3c01009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Recently, deep generative models have been regarded as promising tools in fragment-based drug design (FBDD). Despite the growing interest in these models, they still face challenges in generating molecules with desired properties in low data regimes. In this study, we propose a novel flow-based autoregressive model named FFLOM for linker and R-group design. In a large-scale benchmark evaluation on ZINC, CASF, and PDBbind test sets, FFLOM achieves state-of-the-art performance in terms of validity, uniqueness, novelty, and recovery of the generated molecules and can recover over 92% of the original molecules in the PDBbind test set (with at least five atoms). FFLOM also exhibits excellent potential applicability in several practical scenarios encompassing fragment linking, PROTAC design, R-group growing, and R-group optimization. In all four cases, FFLOM can perfectly reconstruct the ground-truth compounds and generate over 74% of molecules with novel fragments, some of which have higher binding affinity than the ground truth.
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Affiliation(s)
- Jieyu Jin
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Guqin Shi
- Shanghai Qilu Pharmaceutical R&D Center, 576 Libing Road, Pudong New Area District, Shanghai 310115, China
| | - Jingxiao Bao
- Shanghai Qilu Pharmaceutical R&D Center, 576 Libing Road, Pudong New Area District, Shanghai 310115, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Dan Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macau 999078, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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12
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Donyapour N, Fathi Niazi F, Roussey NM, Bose S, Dickson A. Flexible Topology: A Dynamic Model of a Continuous Chemical Space. J Chem Theory Comput 2023; 19:5088-5098. [PMID: 37487141 PMCID: PMC11060842 DOI: 10.1021/acs.jctc.3c00409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Ligand design problems involve searching chemical space for a molecule with a set of desired properties. As chemical space is discrete, this search must be conducted in a pointwise manner, separately investigating one molecule at a time, which can be inefficient. We propose a method called "Flexible Topology", where a ligand is composed of a set of shapeshifting "ghost" atoms, whose atomic identities and connectivity can dynamically change over the course of a simulation. Ghost atoms are guided toward their target positions using a translation-, rotation-, and index-invariant restraint potential. This is the first step toward a continuous model of chemical space, where a dynamic simulation can move from one molecule to another by following gradients of a potential energy function. This builds on a substantial history of alchemy in the field of molecular dynamics simulation, including the Lambda dynamics method developed by Brooks and co-workers [X. Kong and C.L. Brooks III, J. Chem. Phys. 105, 2414 (1996)], but takes it to an extreme by associating a set of four dynamical attributes with each shapeshifting ghost atom that control not only its presence but also its atomic identity. Here, we outline the theoretical details of this method, its implementation using the OpenMM simulation package, and some preliminary studies of ghost particle assembly simulations in vacuum. We examine a set of 10 small molecules, ranging in size from 6 to 50 atoms, and show that Flexible Topology is able to consistently assemble all of these molecules to high accuracy, beginning from randomly initialized positions and attributes.
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Affiliation(s)
- Nazanin Donyapour
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Fatemeh Fathi Niazi
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Nicole M Roussey
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Samik Bose
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Alex Dickson
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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13
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Yang M, Sun H, Liu X, Xue X, Deng Y, Wang X. CMGN: a conditional molecular generation net to design target-specific molecules with desired properties. Brief Bioinform 2023:7165252. [PMID: 37193672 DOI: 10.1093/bib/bbad185] [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: 12/24/2022] [Revised: 04/06/2023] [Accepted: 04/23/2023] [Indexed: 05/18/2023] Open
Abstract
The rational design of chemical entities with desired properties for a specific target is a long-standing challenge in drug design. Generative neural networks have emerged as a powerful approach to sample novel molecules with specific properties, termed as inverse drug design. However, generating molecules with biological activity against certain targets and predefined drug properties still remains challenging. Here, we propose a conditional molecular generation net (CMGN), the backbone of which is a bidirectional and autoregressive transformer. CMGN applies large-scale pretraining for molecular understanding and navigates the chemical space for specified targets by fine-tuning with corresponding datasets. Additionally, fragments and properties were trained to recover molecules to learn the structure-properties relationships. Our model crisscrosses the chemical space for specific targets and properties that control fragment-growth processes. Case studies demonstrated the advantages and utility of our model in fragment-to-lead processes and multi-objective lead optimization. The results presented in this paper illustrate that CMGN has the potential to accelerate the drug discovery process.
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Affiliation(s)
- Minjian Yang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Department of Medicinal Chemistry, Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Hanyu Sun
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Xue Liu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Xi Xue
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., China
| | - Xiaojian Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Department of Medicinal Chemistry, Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
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14
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New avenues in artificial-intelligence-assisted drug discovery. Drug Discov Today 2023; 28:103516. [PMID: 36736583 DOI: 10.1016/j.drudis.2023.103516] [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: 08/07/2022] [Revised: 12/08/2022] [Accepted: 01/26/2023] [Indexed: 02/05/2023]
Abstract
Over the past decade, the amount of biomedical data available has grown at unprecedented rates. Increased automation technology and larger data volumes have encouraged the use of machine learning (ML) or artificial intelligence (AI) techniques for mining such data and extracting useful patterns. Because the identification of chemical entities with desired biological activity is a crucial task in drug discovery, AI technologies have the potential to accelerate this process and support decision making. In addition, the advent of deep learning (DL) has shown great promise in addressing diverse problems in drug discovery, such as de novo molecular design. Herein, we will appraise the current state-of-the-art in AI-assisted drug discovery, discussing the recent applications covering generative models for chemical structure generation, scoring functions to improve binding affinity and pose prediction, and molecular dynamics to assist in the parametrization, featurization and generalization tasks. Finally, we will discuss current hurdles and the strategies to overcome them, as well as potential future directions.
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15
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Dorahy G, Chen JZ, Balle T. Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs. Molecules 2023; 28:molecules28031324. [PMID: 36770990 PMCID: PMC9921936 DOI: 10.3390/molecules28031324] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.
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Affiliation(s)
- Georgia Dorahy
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Jake Zheng Chen
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Thomas Balle
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
- Correspondence:
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16
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom,
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,
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17
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Wang M, Wang J, Weng G, Kang Y, Pan P, Li D, Deng Y, Li H, Hsieh CY, Hou T. ReMODE: a deep learning-based web server for target-specific drug design. J Cheminform 2022; 14:84. [PMID: 36510307 PMCID: PMC9743675 DOI: 10.1186/s13321-022-00665-w] [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: 09/14/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/ .
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Affiliation(s)
- Mingyang Wang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China ,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Jike Wang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China ,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Gaoqi Weng
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China ,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Yu Kang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Peichen Pan
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Dan Li
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Honglin Li
- grid.28056.390000 0001 2163 4895Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237 People’s Republic of China
| | - Chang-Yu Hsieh
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Tingjun Hou
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
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18
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Tan Y, Dai L, Huang W, Guo Y, Zheng S, Lei J, Chen H, Yang Y. DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design. J Chem Inf Model 2022; 62:5907-5917. [PMID: 36404642 DOI: 10.1021/acs.jcim.2c00982] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.
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Affiliation(s)
- Youhai Tan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
| | - Lingxue Dai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
| | - Weifeng Huang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Yinfeng Guo
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Shuangjia Zheng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China.,Galixir Technologies, Beijing100083, China
| | - Jinping Lei
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Hongming Chen
- Guangzhou Laboratory, No. 9 XinDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou510005, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
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19
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Xiong F, Xu H, Yu M, Chen X, Zhong Z, Guo Y, Chen M, Ou H, Wu J, Xie A, Xiong J, Xu L, Zhang L, Zhong Q, Huang L, Li Z, Zhang T, Jin F, He X. 3CLpro inhibitors: DEL-based molecular generation. Front Pharmacol 2022; 13:1085665. [PMID: 36569316 PMCID: PMC9768338 DOI: 10.3389/fphar.2022.1085665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
Molecular generation (MG) via machine learning (ML) has speeded drug structural optimization, especially for targets with a large amount of reported bioactivity data. However, molecular generation for structural optimization is often powerless for new targets. DNA-encoded library (DEL) can generate systematic, target-specific activity data, including novel targets with few or unknown activity data. Therefore, this study aims to overcome the limitation of molecular generation in the structural optimization for the new target. Firstly, we generated molecules using the structure-affinity data (2.96 million samples) for 3C-like protease (3CLpro) from our own-built DEL platform to get rid of using public databases (e.g., CHEMBL and ZINC). Subsequently, to analyze the effect of transfer learning on the positive rate of the molecule generation model, molecular docking and affinity model based on DEL data were applied to explore the enhanced impact of transfer learning on molecule generation. In addition, the generated molecules are subjected to multiple filtering, including physicochemical properties, drug-like properties, and pharmacophore evaluation, molecular docking to determine the molecules for further study and verified by molecular dynamics simulation.
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Affiliation(s)
- Feng Xiong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China,*Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
| | - Honggui Xu
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Mingao Yu
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Xingyu Chen
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Zhenmin Zhong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Yuhan Guo
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Meihong Chen
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Huanfang Ou
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Jiaqi Wu
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Anhua Xie
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Jiaqi Xiong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Linlin Xu
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Lanmei Zhang
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Qijian Zhong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Liye Huang
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Zhenwei Li
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | | | - Feng Jin
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China,*Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
| | - Xun He
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China,*Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
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20
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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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21
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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