1
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Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today 2024; 29:103992. [PMID: 38663579 DOI: 10.1016/j.drudis.2024.103992] [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: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/04/2024]
Abstract
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India
| | - Antonio Lavecchia
- "Drug Discovery" Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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2
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Giorgioni G, Bonifazi A, Botticelli L, Cifani C, Matteucci F, Micioni Di Bonaventura E, Micioni Di Bonaventura MV, Giannella M, Piergentili A, Piergentili A, Quaglia W, Del Bello F. Advances in drug design and therapeutic potential of selective or multitarget 5-HT1A receptor ligands. Med Res Rev 2024. [PMID: 38808959 DOI: 10.1002/med.22049] [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: 12/21/2023] [Revised: 03/14/2024] [Accepted: 05/03/2024] [Indexed: 05/30/2024]
Abstract
5-HT1A receptor (5-HT1A-R) is a serotoninergic G-protein coupled receptor subtype which contributes to several physiological processes in both central nervous system and periphery. Despite being the first 5-HT-R identified, cloned and studied, it still represents a very attractive target in drug discovery and continues to be the focus of a myriad of drug discovery campaigns due to its involvement in numerous neuropsychiatric disorders. The structure-activity relationship studies (SAR) performed over the last years have been devoted to three main goals: (i) design and synthesis of 5-HT1A-R selective/preferential ligands; (ii) identification of 5-HT1A-R biased agonists, differentiating pre- versus post-synaptic agonism and signaling cellular mechanisms; (iii) development of multitarget compounds endowed with well-defined poly-pharmacological profiles targeting 5-HT1A-R along with other serotonin receptors, serotonin transporter (SERT), D2-like receptors and/or enzymes, such as acetylcholinesterase and phosphodiesterase, as a promising strategy for the management of complex psychiatric and neurodegenerative disorders. In this review, medicinal chemistry aspects of ligands acting as selective/preferential or multitarget 5-HT1A-R agonists and antagonists belonging to different chemotypes and developed in the last 7 years (2017-2023) have been discussed. The development of chemical and pharmacological 5-HT1A-R tools for molecular imaging have also been described. Finally, the pharmacological interest of 5-HT1A-R and the therapeutic potential of ligands targeting this receptor have been considered.
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Affiliation(s)
- Gianfabio Giorgioni
- Medicinal Chemistry Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Alessandro Bonifazi
- Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse - Intramural Research Program, National Institutes of Health, Baltimore, Maryland, USA
| | - Luca Botticelli
- Pharmacology Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Carlo Cifani
- Pharmacology Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Federica Matteucci
- Medicinal Chemistry Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | | | | | - Mario Giannella
- Medicinal Chemistry Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | | | - Alessia Piergentili
- Medicinal Chemistry Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Wilma Quaglia
- Medicinal Chemistry Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Fabio Del Bello
- Medicinal Chemistry Unit, School of Pharmacy, University of Camerino, Camerino, Italy
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3
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Lim VJY, Gerber HD, Schihada H, Trinh VT, Hilger D, Vázquez O, Kolb P. A virtual library of small molecules mimicking dipeptides. Arch Pharm (Weinheim) 2024; 357:e2300636. [PMID: 38332463 DOI: 10.1002/ardp.202300636] [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: 11/01/2023] [Revised: 12/13/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
Virtual combinatorial libraries are prevalent in drug discovery due to improvements in the prediction of synthetic reactions that can be performed. This has gone hand in hand with the development of virtual screening capabilities to effectively screen the large chemical spaces spanned by exhaustive enumeration of reaction products. In this study, we generated a small-molecule dipeptide mimic library to target proteins binding small peptides. The library was created based on the general idea of peptide synthesis, that is, amino acid mimics were reacted in silico to form the dipeptide mimics, yielding 2,036,819 unique compounds. After docking calculations, two compounds from the library were synthesized and tested against WD repeat-containing protein 5 (WDR5) and histamine receptors H1-H4 to evaluate whether these molecules are viable in assays. The compounds showed the highest potency at the histamine H3 receptor, with Ki values in the two-digit micromolar range.
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Affiliation(s)
- Victor Jun Yu Lim
- Pharmaceutical Chemistry, Department of Pharmacy, University of Marburg, Marburg, Germany
| | - Hans-Dieter Gerber
- Pharmaceutical Chemistry, Department of Pharmacy, University of Marburg, Marburg, Germany
| | - Hannes Schihada
- Pharmaceutical Chemistry, Department of Pharmacy, University of Marburg, Marburg, Germany
| | - Van Tuan Trinh
- Chemical Biology, Department of Chemistry, University of Marburg, Marburg, Germany
| | - Daniel Hilger
- Pharmaceutical Chemistry, Department of Pharmacy, University of Marburg, Marburg, Germany
| | - Olalla Vázquez
- Chemical Biology, Department of Chemistry, University of Marburg, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), University of Marburg, Marburg, Germany
| | - Peter Kolb
- Pharmaceutical Chemistry, Department of Pharmacy, University of Marburg, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), University of Marburg, Marburg, Germany
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4
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Kong Y, Zhou C, Tan D, Xu X, Li Z, Cheng J. Discovery of Potential Neonicotinoid Insecticides by an Artificial Intelligence Generative Model and Structure-Based Virtual Screening. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:5145-5152. [PMID: 38419506 DOI: 10.1021/acs.jafc.3c06895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
The identification of neonicotinoid insecticides bearing novel scaffolds is of great importance for pesticide discovery. Here, artificial intelligence-based tools and virtual screening strategy were integrated to discover potential leads of neonicotinoid insecticides. A deep generative model was successfully constructed using a recurrent neural network combined with transfer learning. The model evaluation showed that the pretrained model could accurately grasp the SMILES grammar of drug-like molecules and generate potential neonicotinoid compounds after transfer learning. The generated molecules were evaluated by hierarchical virtual screening, hits were subjected to a similarity search, and the most similar structures were purchased for the bioassay. Compounds A2 and A5 displayed 52.5 and 50.3% mortality rates against Aphis craccivora at 100 mg/L, respectively. The docking study indicated that these two compounds have similar binding modes to neonicotinoids, which were verified by further molecular dynamics simulations.
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Affiliation(s)
- Yijin Kong
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Cong Zhou
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Du Tan
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Xiaoyong Xu
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zhong Li
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Jiagao Cheng
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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5
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Kyro GW, Morgunov A, Brent RI, Batista VS. ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation. J Chem Inf Model 2024; 64:653-665. [PMID: 38287889 DOI: 10.1021/acs.jcim.3c01456] [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: 01/31/2024]
Abstract
The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology and demonstrate its applicability to targeted molecular generation. When applied to c-Abl kinase, a protein with FDA-approved small-molecule inhibitors, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. To facilitate implementation and reproducibility, we made all of our software available through the open-source ChemSpaceAL Python package.
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Affiliation(s)
- Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Anton Morgunov
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Rafael I Brent
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
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6
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Cichońska A, Ravikumar B, Rahman R. AI for targeted polypharmacology: The next frontier in drug discovery. Curr Opin Struct Biol 2024; 84:102771. [PMID: 38215530 DOI: 10.1016/j.sbi.2023.102771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/14/2024]
Abstract
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.
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7
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Dollar O, Joshi N, Pfaendtner J, Beck DAC. Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE. J Phys Chem A 2023; 127:7844-7852. [PMID: 37670244 DOI: 10.1021/acs.jpca.3c04188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. We show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.
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Affiliation(s)
- Orion Dollar
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Nisarg Joshi
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - David A C Beck
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
- escience Institute, University of Washington, Seattle, Washington 98195, United States
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8
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Chenthamarakshan V, Hoffman SC, Owen CD, Lukacik P, Strain-Damerell C, Fearon D, Malla TR, Tumber A, Schofield CJ, Duyvesteyn HME, Dejnirattisai W, Carrique L, Walter TS, Screaton GR, Matviiuk T, Mojsilovic A, Crain J, Walsh MA, Stuart DI, Das P. Accelerating drug target inhibitor discovery with a deep generative foundation model. SCIENCE ADVANCES 2023; 9:eadg7865. [PMID: 37343087 DOI: 10.1126/sciadv.adg7865] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.
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Affiliation(s)
| | - Samuel C Hoffman
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, New York, NY, USA
| | - C David Owen
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Petra Lukacik
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Claire Strain-Damerell
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - Tika R Malla
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, UK
| | - Anthony Tumber
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, UK
| | - Christopher J Schofield
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, UK
| | - Helen M E Duyvesteyn
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Wanwisa Dejnirattisai
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Loic Carrique
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Thomas S Walter
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Gavin R Screaton
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | | | | | - Jason Crain
- IBM Research Europe, Hartree Centre, Daresbury WA4 4AD, UK
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK
| | - Martin A Walsh
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA Didcot, UK
| | - David I Stuart
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, OX11 0DE Didcot, UK
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Headington, Oxford, UK
| | - Payel Das
- IBM Research, Thomas J. Watson Research Center, Yorktown Heights, New York, NY, USA
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Liu X, Zhang W, Tong X, Zhong F, Li Z, Xiong Z, Xiong J, Wu X, Fu Z, Tan X, Liu Z, Zhang S, Jiang H, Li X, Zheng M. MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules. J Cheminform 2023; 15:42. [PMID: 37031191 PMCID: PMC10082991 DOI: 10.1186/s13321-023-00711-1] [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: 10/15/2022] [Accepted: 03/14/2023] [Indexed: 04/10/2023] Open
Abstract
Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering method, MolFilterGAN, based on a progressively augmented generative adversarial network. Comparative analysis shows that MolFilterGAN outperforms conventional screening approaches based on drug-likeness or synthetic ability metrics. Retrospective analysis of AI-designed discoidin domain receptor 1 (DDR1) inhibitors shows that MolFilterGAN significantly increases the efficiency of molecular triaging. Further evaluation of MolFilterGAN on eight external ligand sets suggests that MolFilterGAN is useful in triaging or enriching bioactive compounds across a wide range of target types. These results highlighted the importance of MolFilterGAN in evaluating molecules integrally and further accelerating molecular discovery especially combined with advanced AI generative models.
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Affiliation(s)
- Xiaohong Liu
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou, 215128, China
| | - Wei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Feisheng Zhong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Zhaojun Li
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou, 215128, China
| | - Zhaoping Xiong
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jiacheng Xiong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiaolong Wu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Zunyun Fu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Xiaoqin Tan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- ByteDance AI Lab, No. 1999 Yishan Road, Shanghai, 201103, China
| | - Zhiguo Liu
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou, 215128, China
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Hualiang Jiang
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 310024, Hangzhou, China.
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10
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Koutroumpa NM, Papavasileiou KD, Papadiamantis AG, Melagraki G, Afantitis A. A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation. Int J Mol Sci 2023; 24:6573. [PMID: 37047543 PMCID: PMC10095548 DOI: 10.3390/ijms24076573] [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: 12/18/2022] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.
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Affiliation(s)
- Nikoletta-Maria Koutroumpa
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
| | - Konstantinos D. Papavasileiou
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
- Department of ChemoInformatics, NovaMechanics MIKE., 185 45 Piraeus, Greece
| | - Anastasios G. Papadiamantis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 166 73 Vari, Greece
| | - Antreas Afantitis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
- Department of ChemoInformatics, NovaMechanics MIKE., 185 45 Piraeus, Greece
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11
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Westermayr J, Gilkes J, Barrett R, Maurer RJ. High-throughput property-driven generative design of functional organic molecules. NATURE COMPUTATIONAL SCIENCE 2023; 3:139-148. [PMID: 38177626 DOI: 10.1038/s43588-022-00391-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2024]
Abstract
The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures produced through generative deep learning will satisfy these patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (Pareto-)optimal properties by combining a generative deep learning model that predicts three-dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure. Optimization of (multiple) molecular properties is achieved by screening newly generated molecules for desirable electronic properties and reusing hit molecules to retrain the generative model with a bias. The approach is demonstrated to find optimal molecules for organic electronics applications. Our method is generally applicable and eliminates the need for quantum chemical calculations during predictions, making it suitable for high-throughput screening in materials and catalyst design.
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Affiliation(s)
- Julia Westermayr
- Department of Chemistry, University of Warwick, Coventry, UK.
- Wilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Universität Leipzig, Leipzig, Germany.
| | - Joe Gilkes
- Department of Chemistry, University of Warwick, Coventry, UK
- HetSys Centre for Doctoral Training, University of Warwick, Coventry, UK
| | - Rhyan Barrett
- Department of Chemistry, University of Warwick, Coventry, UK
- Wilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Universität Leipzig, Leipzig, Germany
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12
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Multi-target-based polypharmacology prediction (mTPP): An approach using virtual screening and machine learning for multi-target drug discovery. Chem Biol Interact 2022; 368:110239. [DOI: 10.1016/j.cbi.2022.110239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/19/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022]
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13
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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14
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Xie W, Wang F, Li Y, Lai L, Pei J. Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models. J Chem Inf Model 2022; 62:2269-2279. [PMID: 35544331 DOI: 10.1021/acs.jcim.2c00042] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A persistent goal for de novo drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for de novo drug design.
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Affiliation(s)
- Weixin Xie
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Fanhao Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yibo Li
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Peking-Tsinghua Center for Life Science at BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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15
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Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen KF. Generative models for molecular discovery: Recent advances and challenges. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Camille Bilodeau
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Wengong Jin
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Tommi Jaakkola
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Regina Barzilay
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
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16
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Du BX, Qin Y, Jiang YF, Xu Y, Yiu SM, Yu H, Shi JY. Compound–protein interaction prediction by deep learning: Databases, descriptors and models. Drug Discov Today 2022; 27:1350-1366. [DOI: 10.1016/j.drudis.2022.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/19/2021] [Accepted: 02/28/2022] [Indexed: 11/24/2022]
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17
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Martín-Cámara O, Arribas M, Wells G, Morales-Tenorio M, Martín-Requero Á, Porras G, Martínez A, Giorgi G, López-Alvarado P, Lastres-Becker I, Menéndez JC. Multitarget Hybrid Fasudil Derivatives as a New Approach to the Potential Treatment of Amyotrophic Lateral Sclerosis. J Med Chem 2022; 65:1867-1882. [PMID: 34985276 PMCID: PMC9132363 DOI: 10.1021/acs.jmedchem.1c01255] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Hybrid compounds containing structural fragments of the Rho kinase inhibitor fasudil and the NRF2 inducers caffeic and ferulic acids were designed with the aid of docking and molecular mechanics studies. Following the synthesis of the compounds using a peptide-coupling methodology, they were characterized for their ROCK2 inhibition, radical scavenging, effects on cell viability (MTT assay), and NRF2 induction (luciferase assay). One of the compounds (1d) was selected in view of its good multitarget profile and good tolerability. It was able to induce the NRF2 signature, promoting the expression of the antioxidant response enzymes HO-1 and NQO1, via a KEAP1-dependent mechanism. Analysis of mRNA and protein levels of the NRF2 pathway showed that 1d induced the NRF2 signature in control and SOD1-ALS lymphoblasts but not in sALS, where it was already increased in the basal state. These results show the therapeutic potential of this compound, especially for ALS patients with a SOD1 mutation.
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Affiliation(s)
- Olmo Martín-Cámara
- Unidad de Química Orgánica y Farmacéutica, Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense, Plaza de Ramón y Cajal sn, 28040 Madrid, Spain
| | - Marina Arribas
- Instituto de Investigaciones Biomédicas "Alberto Sols" UAM-CSIC, Department of Biochemistry, School of Medicine, and Institute Teófilo Hernando for Drug Discovery, Universidad Autónoma de Madrid, 28029 Madrid, Spain
| | - Geoffrey Wells
- UCL School of Pharmacy, University College London, 29/39 Brunswick Square, London WC1N 1AX, United Kingdom
| | - Marcos Morales-Tenorio
- Centro de Investigaciones Biológicas Margarita Salas, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain
| | - Ángeles Martín-Requero
- Centro de Investigaciones Biológicas Margarita Salas, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031 Madrid, Spain
| | - Gracia Porras
- Centro de Investigaciones Biológicas Margarita Salas, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain
| | - Ana Martínez
- Centro de Investigaciones Biológicas Margarita Salas, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031 Madrid, Spain
| | - Giorgio Giorgi
- Unidad de Química Orgánica y Farmacéutica, Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense, Plaza de Ramón y Cajal sn, 28040 Madrid, Spain
| | - Pilar López-Alvarado
- Unidad de Química Orgánica y Farmacéutica, Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense, Plaza de Ramón y Cajal sn, 28040 Madrid, Spain
| | - Isabel Lastres-Becker
- Instituto de Investigaciones Biomédicas "Alberto Sols" UAM-CSIC, Department of Biochemistry, School of Medicine, and Institute Teófilo Hernando for Drug Discovery, Universidad Autónoma de Madrid, 28029 Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031 Madrid, Spain
| | - J Carlos Menéndez
- Unidad de Química Orgánica y Farmacéutica, Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense, Plaza de Ramón y Cajal sn, 28040 Madrid, Spain
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18
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Castro LHE, Sant'Anna CMR. Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications. Curr Top Med Chem 2021; 22:333-346. [PMID: 34844540 DOI: 10.2174/1568026621666211129140958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/23/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.
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Affiliation(s)
| | - Carlos Mauricio R Sant'Anna
- Programa de Pós-Graduação em Química, Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica. Brazil
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Sousa T, Correia J, Pereira V, Rocha M. Generative Deep Learning for Targeted Compound Design. J Chem Inf Model 2021; 61:5343-5361. [PMID: 34699719 DOI: 10.1021/acs.jcim.0c01496] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications.
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Affiliation(s)
- Tiago Sousa
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - João Correia
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - Vítor Pereira
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
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Tong X, Liu X, Tan X, Li X, Jiang J, Xiong Z, Xu T, Jiang H, Qiao N, Zheng M. Generative Models for De Novo Drug Design. J Med Chem 2021; 64:14011-14027. [PMID: 34533311 DOI: 10.1021/acs.jmedchem.1c00927] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) is booming. Among various AI approaches, generative models have received much attention in recent years. Inspired by these successes, researchers are now applying generative model techniques to de novo drug design, which has been considered as the "holy grail" of drug discovery. In this Perspective, we first focus on describing models such as recurrent neural network, autoencoder, generative adversarial network, transformer, and hybrid models with reinforcement learning. Next, we summarize the applications of generative models to drug design, including generating various compounds to expand the compound library and designing compounds with specific properties, and we also list a few publicly available molecular design tools based on generative models which can be used directly to generate molecules. In addition, we also introduce current benchmarks and metrics frequently used for generative models. Finally, we discuss the challenges and prospects of using generative models to aid drug design.
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Affiliation(s)
- Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xiaohong Liu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xiaoqin Tan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Jiaxin Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Zhaoping Xiong
- Laboratory of Health Intelligence, Huawei Technologies Co., Ltd, Shenzhen 518100, China
| | | | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Nan Qiao
- Laboratory of Health Intelligence, Huawei Technologies Co., Ltd, Shenzhen 518100, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
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21
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Rema J, Novais F, Telles-Correia D. Precision Psychiatry: Machine learning as a tool to find new pharmacological targets. Curr Top Med Chem 2021; 22:1261-1269. [PMID: 34607546 DOI: 10.2174/1568026621666211004095917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/20/2021] [Accepted: 08/19/2021] [Indexed: 12/18/2022]
Abstract
There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs. OBJECTIVE The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets. METHODS Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review. RESULTS The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents. CONCLUSION Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.
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Affiliation(s)
- João Rema
- Faculdade de Medicina da Universidade de Lisboa. Portugal
| | - Filipa Novais
- Faculdade de Medicina da Universidade de Lisboa. Portugal
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22
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Mangiatordi GF, Intranuovo F, Delre P, Abatematteo FS, Abate C, Niso M, Creanza TM, Ancona N, Stefanachi A, Contino M. Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration. J Med Chem 2020; 63:14448-14469. [PMID: 33094613 DOI: 10.1021/acs.jmedchem.0c01357] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.
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Affiliation(s)
| | - Francesca Intranuovo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Pietro Delre
- CNR-Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy.,Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Francesca Serena Abatematteo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Carmen Abate
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Mauro Niso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Teresa Maria Creanza
- CNR-Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Nicola Ancona
- CNR-Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Angela Stefanachi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Marialessandra Contino
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
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