1
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Chen S, Xie J, Ye R, Xu DD, Yang Y. Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation. Chem Sci 2024; 15:10366-10380. [PMID: 38994407 PMCID: PMC11234869 DOI: 10.1039/d4sc00094c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 06/09/2024] [Indexed: 07/13/2024] Open
Abstract
Dual-target drug design has gained significant attention in the treatment of complex diseases, such as cancers and autoimmune disorders. A widely employed design strategy is combining pharmacophores to leverage the knowledge of structure-activity relationships of both targets. Unfortunately, pharmacophore combination often struggles with long and expensive trial and error, because the protein pockets of the two targets impose complex structural constraints. In this study, we propose AIxFuse, a structure-aware dual-target drug design method that learns pharmacophore fusion patterns to satisfy the dual-target structural constraints simulated by molecular docking. AIxFuse employs two self-play reinforcement learning (RL) agents to learn pharmacophore selection and fusion by comprehensive feedback including dual-target molecular docking scores. Collaboratively, the molecular docking scores are learned by active learning (AL). Through collaborative RL and AL, AIxFuse learns to generate molecules with multiple desired properties. AIxFuse is shown to outperform state-of-the-art methods in generating dual-target drugs against glycogen synthase kinase-3 beta (GSK3β) and c-Jun N-terminal kinase 3 (JNK3). When applied to another task against retinoic acid receptor-related orphan receptor γ-t (RORγt) and dihydroorotate dehydrogenase (DHODH), AIxFuse exhibits consistent performance while compared methods suffer from performance drops, leading to a 5 times higher performance in success rate. Docking studies demonstrate that AIxFuse can generate molecules concurrently satisfying the binding mode required by both targets. Further free energy perturbation calculation indicates that the generated candidates have promising binding free energies against both targets.
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Affiliation(s)
- Sheng Chen
- School of Computer Science and Engineering, Sun Yat-sen University Guangzhou 510006 China
- AixplorerBio Inc. Jiaxing 314031 China
| | - Junjie Xie
- School of Computer Science and Engineering, Sun Yat-sen University Guangzhou 510006 China
- AixplorerBio Inc. Jiaxing 314031 China
| | | | | | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University Guangzhou 510006 China
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2
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Xia X, Liu Y, Zheng C, Zhang X, Wu Q, Gao X, Zeng X, Su Y. Evolutionary Multiobjective Molecule Optimization in an Implicit Chemical Space. J Chem Inf Model 2024; 64:5161-5174. [PMID: 38870455 PMCID: PMC11235097 DOI: 10.1021/acs.jcim.4c00031] [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: 01/05/2024] [Revised: 05/08/2024] [Accepted: 05/13/2024] [Indexed: 06/15/2024]
Abstract
Optimization techniques play a pivotal role in advancing drug development, serving as the foundation of numerous generative methods tailored to efficiently design optimized molecules derived from existing lead compounds. However, existing methods often encounter difficulties in generating diverse, novel, and high-property molecules that simultaneously optimize multiple drug properties. To overcome this bottleneck, we propose a multiobjective molecule optimization framework (MOMO). MOMO employs a specially designed Pareto-based multiproperty evaluation strategy at the molecular sequence level to guide the evolutionary search in an implicit chemical space. A comparative analysis of MOMO with five state-of-the-art methods across two benchmark multiproperty molecule optimization tasks reveals that MOMO markedly outperforms them in terms of diversity, novelty, and optimized properties. The practical applicability of MOMO in drug discovery has also been validated on four challenging tasks in the real-world discovery problem. These results suggest that MOMO can provide a useful tool to facilitate molecule optimization problems with multiple properties.
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Affiliation(s)
- Xin Xia
- The
Key Laboratory of Intelligent Computing and Signal Processing of Ministry
of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
- Institute
of Artificial Intelligence, Hefei Comprehensive
National Science Center, 5089 Wangjiang West Road, Hefei 230088, AnhuiChina
| | - Yiping Liu
- College
of Computer Science and Electronic Engineering, Hunan University, Changsha 410012, China
| | - Chunhou Zheng
- The
Key Laboratory of Intelligent Computing and Signal Processing of Ministry
of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Xingyi Zhang
- The
Key Laboratory of Intelligent Computing and Signal Processing of Ministry
of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Qingwen Wu
- The
Key Laboratory of Intelligent Computing and Signal Processing of Ministry
of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Xin Gao
- Computer
Science Program, Computer, Electrical and Mathematical Sciences and
Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology
(KAUST), Thuwal 23955-6900, Kingdom
of Saudi Arabia
| | - Xiangxiang Zeng
- College
of Computer Science and Electronic Engineering, Hunan University, Changsha 410012, China
| | - Yansen Su
- The
Key Laboratory of Intelligent Computing and Signal Processing of Ministry
of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
- Institute
of Artificial Intelligence, Hefei Comprehensive
National Science Center, 5089 Wangjiang West Road, Hefei 230088, AnhuiChina
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3
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Retchin M, Wang Y, Takaba K, Chodera JD. DrugGym: A testbed for the economics of autonomous drug discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596296. [PMID: 38854082 PMCID: PMC11160604 DOI: 10.1101/2024.05.28.596296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.
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Affiliation(s)
- Michael Retchin
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Yuanqing Wang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Simons Center for Computational Chemistry and Center for Data Science, New York University, New York, NY 10004
| | - Kenichiro Takaba
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation, Shizuoka 410-2321, Japan
| | - John D. Chodera
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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4
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Thomas M, O'Boyle NM, Bender A, De Graaf C. MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design. J Cheminform 2024; 16:64. [PMID: 38816825 PMCID: PMC11141043 DOI: 10.1186/s13321-024-00861-w] [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: 11/21/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index.Scientific ContributionMolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Noel M O'Boyle
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Chris De Graaf
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
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5
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Ai H, Wu D, Zhou H, Xu J, Gu Q. dMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks. J Chem Inf Model 2024; 64:3744-3755. [PMID: 38662925 DOI: 10.1021/acs.jcim.4c00391] [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: 05/14/2024]
Abstract
Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.
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Affiliation(s)
- Haopeng Ai
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Deyin Wu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Huihao Zhou
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Jun Xu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Qiong Gu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
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6
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Shahab M, de Farias Morais GC, Akash S, Fulco UL, Oliveira JIN, Zheng G, Akter S. A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis. J Cell Mol Med 2024; 28:e18279. [PMID: 38634203 PMCID: PMC11024510 DOI: 10.1111/jcmm.18279] [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: 11/29/2023] [Revised: 03/12/2024] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
The rise of pyrazinamide (PZA)-resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA-resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost-effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through in vitro and in vivo experiments.
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Affiliation(s)
- Muhammad Shahab
- State key laboratories of Chemical Resources Engineering Beijing, University of Chemical TechnologyBeijingChina
| | | | - Shopnil Akash
- Department of PharmacyDaffodil International UniversityDhakaBangladesh
| | - Umberto Laino Fulco
- Department of Biophysics and Pharmacology, Bioscience CenterFederal University of Rio Grande do NorteNatalRio Grande do NorteBrazil
| | - Jonas Ivan Nobre Oliveira
- Department of Biophysics and Pharmacology, Bioscience CenterFederal University of Rio Grande do NorteNatalRio Grande do NorteBrazil
| | - Guojun Zheng
- State key laboratories of Chemical Resources Engineering Beijing, University of Chemical TechnologyBeijingChina
| | - Shahina Akter
- Bangladesh Council of Scientific and Industrial ResearchDhakaBangladesh
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7
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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8
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Wu P, Du H, Yan Y, Lee TY, Bai C, Wu S. Guided diffusion for molecular generation with interaction prompt. Brief Bioinform 2024; 25:bbae174. [PMID: 38647154 PMCID: PMC11033848 DOI: 10.1093/bib/bbae174] [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: 09/25/2023] [Revised: 03/16/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Molecular generative models have exhibited promising capabilities in designing molecules from scratch with high binding affinities in a predetermined protein pocket, offering potential synergies with traditional structural-based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies and have been recognized as appealing targets for designed molecules. In this work, we develop an interaction prompt guided diffusion model, InterDiff to deal with the challenges. Four kinds of atomic interactions are involved in our model and represented as learnable vector embeddings. These embeddings serve as conditions for individual residue to guide the molecular generative process. Comprehensive in silico experiments evince that our model could generate molecules with desired ligand-protein interactions in a guidable way. Furthermore, we validate InterDiff on two realistic protein-based therapeutic agents. Results show that InterDiff could generate molecules with better or similar binding mode compared to known targeted drugs.
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Affiliation(s)
- Peng Wu
- Department of Urology, South China Hospital, Medical School, Shenzhen University, Fuxin Road, Longgang District, Shenzhen, 518116, China. Tel.: +86 0755 89798999
| | - Huabin Du
- MoMed Biotechnology Co., Ltd., Hangzhou 310005, China
| | - Yingchao Yan
- MoMed Biotechnology Co., Ltd., Hangzhou 310005, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan, China. Tel.:+886 0928 560313
| | - Chen Bai
- MoMed Biotechnology Co., Ltd., Hangzhou 310005, China
- Warshel Institute for Computational Biology, School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, Guangdong, China. Tel.:+86 0755 84273118
| | - Song Wu
- Department of Urology, South China Hospital, Medical School, Shenzhen University, Fuxin Road, Longgang District, Shenzhen, 518116, China. Tel.: +86 0755 89798999
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
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9
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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10
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Higgins WT, Vibhute S, Bennett C, Lindert S. Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods. J Chem Inf Model 2024; 64:435-448. [PMID: 38175956 DOI: 10.1021/acs.jcim.3c01358] [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: 01/06/2024]
Abstract
We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.
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Affiliation(s)
- William T Higgins
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Sandip Vibhute
- Medicinal Chemistry Shared Resource, Comprehensive Cancer Center, Ohio State University, Columbus, Ohio 43210, United States
| | - Chad Bennett
- Medicinal Chemistry Shared Resource, Comprehensive Cancer Center, Ohio State University, Columbus, Ohio 43210, United States
- Drug Development Institute, Ohio State University, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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11
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Flachsenberg F, Ehrt C, Gutermuth T, Rarey M. Redocking the PDB. J Chem Inf Model 2024; 64:219-237. [PMID: 38108627 DOI: 10.1021/acs.jcim.3c01573] [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: 12/19/2023]
Abstract
Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).
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Affiliation(s)
- Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Torben Gutermuth
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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12
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Jin Z, Wei Z. Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends. Compr Rev Food Sci Food Saf 2024; 23:e13280. [PMID: 38284571 DOI: 10.1111/1541-4337.13280] [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: 08/07/2023] [Revised: 11/19/2023] [Accepted: 11/22/2023] [Indexed: 01/30/2024]
Abstract
In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.
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Affiliation(s)
- Zihan Jin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Zihao Wei
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, China
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13
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Wu J, Lv J, Zhao L, Zhao R, Gao T, Xu Q, Liu D, Yu Q, Ma F. Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167028. [PMID: 37704131 DOI: 10.1016/j.scitotenv.2023.167028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/03/2023] [Accepted: 09/10/2023] [Indexed: 09/15/2023]
Abstract
Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.
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Affiliation(s)
- Jieting Wu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Jin Lv
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Lei Zhao
- State Key Laboratory of Urban Water Resources & Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Ruofan Zhao
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Tian Gao
- Key Laboratory of Integrated Regulation and Resource Development of Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Xikang Road #1, Nanjing 210098, China
| | - Qi Xu
- PetroChina Fushun Petrochemical Company, Fushun 113000, China
| | - Dongbo Liu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Qiqi Yu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Fang Ma
- State Key Laboratory of Urban Water Resources & Environment, Harbin Institute of Technology, Harbin 150090, China.
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14
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Samantasinghar A, Ahmed F, Rahim CSA, Kim KH, Kim S, Choi KH. Artificial intelligence-assisted repurposing of lubiprostone alleviates tubulointerstitial fibrosis. Transl Res 2023; 262:75-88. [PMID: 37541485 DOI: 10.1016/j.trsl.2023.07.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023]
Abstract
Tubulointerstitial fibrosis (TIF) is the most prominent cause which leads to chronic kidney disease (CKD) and end-stage renal failure. Despite extensive research, there have been many clinical trial failures, and there is currently no effective treatment to cure renal fibrosis. This demonstrates the necessity of more effective therapies and better preclinical models to screen potential drugs for TIF. In this study, we investigated the antifibrotic effect of the machine learning-based repurposed drug, lubiprostone, validated through an advanced proximal tubule on a chip system and in vivo UUO mice model. Lubiprostone significantly downregulated TIF biomarkers including connective tissue growth factor (CTGF), extracellular matrix deposition (Fibronectin and collagen), transforming growth factor (TGF-β) downstream signaling markers especially, Smad-2/3, matrix metalloproteinase (MMP2/9), plasminogen activator inhibitor-1 (PAI-1), EMT and JAK/STAT-3 pathway expression in the proximal tubule on a chip model and UUO model compared to the conventional 2D culture. These findings suggest that the proximal tubule on a chip model is a more physiologically relevant model for studying and identifying potential biomarkers for fibrosis compared to conventional in vitro 2D culture and alternative of an animal model. In conclusion, the high throughput Proximal tubule-on-chip system shows improved in vivo-like function and indicates the potential utility for renal fibrosis drug screening. Additionally, repurposed Lubiprostone shows an effective potency to treat TIF via inhibiting 3 major profibrotic signaling pathways such as TGFβ/Smad, JAK/STAT, and epithelial-mesenchymal transition (EMT), and restores kidney function.
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Affiliation(s)
| | - Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
| | | | | | - Sejoong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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15
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Minibaeva G, Ivanova A, Polishchuk P. EasyDock: customizable and scalable docking tool. J Cheminform 2023; 15:102. [PMID: 37915072 PMCID: PMC10619229 DOI: 10.1186/s13321-023-00772-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/21/2023] [Indexed: 11/03/2023] Open
Abstract
Docking of large compound collections becomes an important procedure to discover new chemical entities. Screening of large sets of compounds may also occur in de novo design projects guided by molecular docking. To facilitate these processes, there is a need for automated tools capable of efficiently docking a large number of molecules using multiple computational nodes within a reasonable timeframe. These tools should also allow for easy integration of new docking programs and provide a user-friendly program interface to support the development of further approaches utilizing docking as a foundation. Currently available tools have certain limitations, such as lacking a convenient program interface or lacking support for distributed computations. In response to these limitations, we have developed a module called EasyDock. It can be deployed over a network of computational nodes using the Dask library, without requiring a specific cluster scheduler. Furthermore, we have proposed and implemented a simple model that predicts the runtime of docking experiments and applied it to minimize overall docking time. The current version of EasyDock supports popular docking programs, namely Autodock Vina, gnina, and smina. Additionally, we implemented a supplementary feature to enable docking of boron-containing compounds, which are not inherently supported by Vina and smina, and demonstrated its applicability on a set of 55 PDB protein-ligand complexes.
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Affiliation(s)
- Guzel Minibaeva
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Aleksandra Ivanova
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Pavel Polishchuk
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic.
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16
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Kerstjens A, De Winter H. A molecule perturbation software library and its application to study the effects of molecular design constraints. J Cheminform 2023; 15:89. [PMID: 37752561 PMCID: PMC10523775 DOI: 10.1186/s13321-023-00761-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
Computational molecular design can yield chemically unreasonable compounds when performed carelessly. A popular strategy to mitigate this risk is mimicking reference chemistry. This is commonly achieved by restricting the way in which molecules are constructed or modified. While it is well established that such an approach helps in designing chemically appealing molecules, concerns about these restrictions impacting chemical space exploration negatively linger. In this work we present a software library for constrained graph-based molecule manipulation and showcase its functionality by developing a molecule generator. Said generator designs molecules mimicking reference chemical features of differing granularity. We find that restricting molecular construction lightly, beyond the usual positive effects on drug-likeness and synthesizability of designed molecules, provides guidance to optimization algorithms navigating chemical space. Nonetheless, restricting molecular construction excessively can indeed hinder effective chemical space exploration.
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Affiliation(s)
- Alan Kerstjens
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitslaan 1, 2610, Wilrijk, Belgium
| | - Hans De Winter
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitslaan 1, 2610, Wilrijk, Belgium.
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17
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Axelrod S, Shakhnovich E, Gómez-Bombarelli R. Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling. J Chem Inf Model 2023; 63:5794-5802. [PMID: 37671878 DOI: 10.1021/acs.jcim.3c00484] [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: 09/07/2023]
Abstract
Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and biological properties that must be simultaneously optimized. To accelerate the design of photoactive drugs, we describe a procedure that combines ligand-protein docking with chemical property prediction based on machine learning (ML). We apply this procedure to 58 proteins and 9000 photo-drug candidates based on azobenzene cis-trans isomerism. We find that most proteins display a preference for trans isomers over cis and that the binding affinities of nominally active/inactive pairs are in fact highly correlated. These findings have significant value for photopharmacology research, and reinforce the need for virtual screening to identify compounds with rare desirable properties. Further, we combine our procedure with quantum chemical validation to identify promising candidates for the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer cells. The top compounds are predicted to have long-lived active forms, differential bioactivity, and absorption in the near-infrared therapeutic window.
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Affiliation(s)
- Simon Axelrod
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Eugene Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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18
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Hagg A, Kirschner KN. Open-Source Machine Learning in Computational Chemistry. J Chem Inf Model 2023; 63:4505-4532. [PMID: 37466636 PMCID: PMC10430767 DOI: 10.1021/acs.jcim.3c00643] [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] [Received: 05/04/2023] [Indexed: 07/20/2023]
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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Affiliation(s)
- Alexander Hagg
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Electrical Engineering, Mechanical Engineering and Technical Journalism, University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
| | - Karl N. Kirschner
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Computer Science, University of Applied
Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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19
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Lu QP, Wu ML, Li HL, Zhou Y, Xian MH, Huang WZ, Piao XH, Ge YW. Combined Metabolite Analysis and Network Pharmacology to Elucidate the Mechanisms of Therapeutic Effect of Melastoma dodecandrum Ellagitannins on Abnormal Uterine Bleeding. Chem Biodivers 2023; 20:e202300646. [PMID: 37358391 DOI: 10.1002/cbdv.202300646] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 06/27/2023]
Abstract
The abnormal uterine bleeding (AUB) is complex and usually leads to severe anemia. Melastomadodecandrum (MD) is clinically used for the treatment of metrorrhagia bleeding. The MD ellagitannins (MD-ETs) had been evidenced being effective at hemorrhage, and exerts biological activities upon their metabolites including ellagic acid and urolithins. In this study, the blood-permeated metabolites from theMD-ETs were analyzed using LC-MS approach, and 19 metabolites including ellagic acid and urolithin A derivatives were identified. Furthermore, a network pharmacology analysis including the target prediction analysis, AUB target analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to reveal the relationships between "metabolites-targets-pathways", which was further verified by molecular docking analysis. The results showed that methyl ellagic acid, urolithin A and isourolithin A produced from MD-ETs can be absorbed into the blood, and might act on the core targets of VEGFA, SRC, MTOR, EGFR and CCND1. And the hemostatic effects were exerted through PI3K-Akt, endocrine resistance and Rap 1 signaling pathways. These results implied the potential effective constituents and action mechanism of MD-ETs in the therapy of AUB, which will promote the application of MD-ETs as natural agent for the treatment of gynecological bleeding diseases.
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Affiliation(s)
- Qiu-Ping Lu
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, 510006, China
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of National Administration of TCM, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Miao-Li Wu
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, 510006, China
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of National Administration of TCM, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Hui-Lin Li
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, 510006, China
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of National Administration of TCM, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Yu Zhou
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, 510006, China
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of National Administration of TCM, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Ming-Hua Xian
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, 510006, China
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of National Administration of TCM, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Wei-Zhong Huang
- Guangdong Luofushan Sinopharm Co., Ltd., Huizhou, 516133, China
| | - Xiu-Hong Piao
- School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Yue-Wei Ge
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, 510006, China
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of National Administration of TCM, Guangdong Pharmaceutical University, Guangzhou, 510006, China
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20
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Wang Z, Pu Q, Li Y. Bidirectional selection of the functional properties and environmental friendliness of organophosphorus (OP) pesticide derivatives: Design, screening, and mechanism analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 879:163043. [PMID: 36963678 DOI: 10.1016/j.scitotenv.2023.163043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 05/17/2023]
Abstract
Organophosphorus pesticides (OPs) are widely used in agricultural production, but the resulting pollution and drug resistance have sparked widespread concern. Therefore, this paper built a model to design OP substitute molecules with high functionality and environmental friendliness, as well as conducted various human health and ecological environment evaluations, synthetic accessibility screening, and easy detection screening. The functionality of the two OP substitute molecules, DIM-100 and DIM-164, increased by 22.79 % and 22.18 %, respectively, and the environmental friendliness increased by 18.07 % and 24.02 %, respectively. The human health risk and ecological, environmental risks were significantly reduced. Both molecules are easy to synthesize, and their detection sensitivity is 9.85 % and 11.24 % higher than that of the target molecule, respectively. Furthermore, significant changes in the distribution of electrons and holes near the C8 and S1 atoms of the OP substitute molecule resulted in easier breakage of the C8-S1 bond, enhancing its photodegradation ability. The charge transfer ability between the atoms of the molecule (as increasing the electron-withdrawing group led to an increase in charge of the P atom) and the volume of the cholinesterase active pocket both affect the functionality of the DIM substitute molecule. That is, the volume of the cholinesterase active pocket of the bee is smaller than that of the brown planthopper and is more affected by the volume of the OP molecule. Furthermore, the mutual verification analysis of the bidirectional selectivity effect of OP substitute molecules between the BayesianRidge model and the 3D-QS(A2 + ∀3)R model reveals that the overall charge transfer degree of DIM substitute molecules is the main reason for the increase in the bidirectional selectivity effect.
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Affiliation(s)
- Zhonghe Wang
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Qikun Pu
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Yu Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China.
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21
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Janet JP, Mervin L, Engkvist O. Artificial intelligence in molecular de novo design: Integration with experiment. Curr Opin Struct Biol 2023; 80:102575. [PMID: 36966692 DOI: 10.1016/j.sbi.2023.102575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/09/2023] [Accepted: 02/18/2023] [Indexed: 06/04/2023]
Abstract
In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progress and experimental validation of novel generative algorithms, the validation of QSAR models and how AI-based molecular de novo design is starting to become connected with chemistry automation. While progress has been made in the last few years, it is still early days. The experimental validations conducted thus far should be considered proof-of-principle, providing confidence that the field is moving in the right direction.
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Affiliation(s)
- Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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22
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Ciepliński T, Danel T, Podlewska S, Jastrzȩbski S. Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark. J Chem Inf Model 2023. [PMID: 37224003 DOI: 10.1021/acs.jcim.2c01355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a widely used computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that various graph-based generative models fail to propose molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we also include simpler tasks in the benchmark based on a simpler scoring function. We release the benchmark as an easy to use package available at https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone toward the goal of automatically generating promising drug candidates.
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Affiliation(s)
- Tobiasz Ciepliński
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
| | - Tomasz Danel
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Smȩtna 12, 31-343 Kraków, Poland
| | - Stanisław Jastrzȩbski
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
- Molecule.one, Al. Jerozolimskie 96, 00-807 Warsaw, Poland
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23
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Thomas M, Bender A, de Graaf C. Integrating structure-based approaches in generative molecular design. Curr Opin Struct Biol 2023; 79:102559. [PMID: 36870277 DOI: 10.1016/j.sbi.2023.102559] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/23/2023] [Accepted: 01/31/2023] [Indexed: 03/06/2023]
Abstract
Generative molecular design for drug discovery and development has seen a recent resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by computationally exploring much larger chemical spaces than traditional virtual screening techniques. However, most generative models thus far have only utilized small-molecule information to train and condition de novo molecule generators. Here, we instead focus on recent approaches that incorporate protein structure into de novo molecule optimization in an attempt to maximize the predicted on-target binding affinity of generated molecules. We summarize these structure integration principles into either distribution learning or goal-directed optimization and for each case whether the approach is protein structure-explicit or implicit with respect to the generative model. We discuss recent approaches in the context of this categorization and provide our perspective on the future direction of the field.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. https://twitter.com/@AndreasBenderUK
| | - Chris de Graaf
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK. https://twitter.com/@Chris_de_Graaf
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24
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Yu Y, Xu S, He R, Liang G. Application of Molecular Simulation Methods in Food Science: Status and Prospects. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:2684-2703. [PMID: 36719790 DOI: 10.1021/acs.jafc.2c06789] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.
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Affiliation(s)
- Yuandong Yu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Shiqi Xu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Ran He
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
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25
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Network Pharmacology and Molecular Docking Analysis on Molecular Targets and Mechanisms of Bushen Hugu Decoction in the Treatment of Malignant Tumor Bone Metastases. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2055900. [DOI: 10.1155/2022/2055900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/04/2022] [Accepted: 11/05/2022] [Indexed: 11/18/2022]
Abstract
Purpose. To explore the active compounds of the Chinese medicine prescriptions of Bushen Hugu Decoction (BHD) and demonstrate its mechanisms against malignant tumor bone metastasis (BM) through network pharmacology and molecular docking analysis.Methods. The main components and targets of BHD were retrieved from the TCMSP database, and the targets were normalized by UniProt. The Herbs-Components-Targets network of BHD was established by Cytoscape. The main BM targets were obtained from GeneCards, TTD, DrugBank, and OMIM. STRING and Cytoscape were used to construct a PPI network and obtain hub genes. DAVID and Metascape were used for GO and KEGG enrichment analyses. According to the network topology parameters, the top 4 components were selected for molecular docking verification with the core targets. Results. Compound–target network of BHD mainly contained 51 compounds and 259 corresponding targets including 107 BHD-BM targets. PPI interaction network and subnetworks identified ten hub genes. GO enrichment analysis found 1970 terms (
), and 164 signaling pathways (
) were found in KEGG, including PI3K-Akt signaling pathway, proteoglycans in cancer, prostate cancer, MAPK signaling pathway, and IL-17 signaling pathway. Molecular docking analysis showed that the active components of BHD, quercetin, luteolin, kaempferol, and aureusidin have good binding activity to the core targets. Conclusion. The potential molecular target and signaling pathways were found for BHD major active components. It provides guidance for the future mechanism research of the BHD in malignant tumor bone metastasis. This study also established the foundation for the new strategy for the pharmacology study of Chinese medicine.
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26
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Sauer S, Matter H, Hessler G, Grebner C. Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods. Front Chem 2022; 10:1012507. [PMID: 36339033 PMCID: PMC9629386 DOI: 10.3389/fchem.2022.1012507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/20/2022] [Indexed: 11/14/2022] Open
Abstract
The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into “drug-like” chemical space, such as target-activity machine learning models, respectively.
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Graff DE, Aldeghi M, Morrone JA, Jordan KE, Pyzer-Knapp EO, Coley CW. Self-Focusing Virtual Screening with Active Design Space Pruning. J Chem Inf Model 2022; 62:3854-3862. [DOI: 10.1021/acs.jcim.2c00554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- David E. Graff
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
| | - Matteo Aldeghi
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
| | - Joseph A. Morrone
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10594, United States
| | - Kirk E. Jordan
- IBM Thomas J. Watson Research Center, Cambridge, Massachusetts 02142, United States
| | | | - Connor W. Coley
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts 02142, United States
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