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Ai C, Yang H, Liu X, Dong R, Ding Y, Guo F. MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning. PLoS Comput Biol 2024; 20:e1012229. [PMID: 38924082 PMCID: PMC11233020 DOI: 10.1371/journal.pcbi.1012229] [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: 07/04/2023] [Revised: 07/09/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
De novo drug design is crucial in advancing drug discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model's generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery.
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Affiliation(s)
- Chengwei Ai
- School of computer science and engineering, Central South University, Changsha, China
| | - Hongpeng Yang
- Department of computer science and engineering, University of South Carolina, Columbia, South Carolina, United States of America
| | - Xiaoyi Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
- Ministry of Education, Engineering Research Center for Pharmaceutics of Chinese Materia Medica and New Drug Development, Beijing, China
| | - Ruihan Dong
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Fei Guo
- School of computer science and engineering, Central South University, Changsha, China
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2
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Krishnan SR, Bung N, Srinivasan R, Roy A. Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process. J Mol Graph Model 2024; 129:108734. [PMID: 38442440 DOI: 10.1016/j.jmgm.2024.108734] [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: 12/03/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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3
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Hossain A, Rahman ME, Faruqe MO, Saif A, Suhi S, Zaman R, Hirad AH, Matin MN, Rabbee MF, Baek KH. Characterization of Plant-Derived Natural Inhibitors of Dipeptidyl Peptidase-4 as Potential Antidiabetic Agents: A Computational Study. Pharmaceutics 2024; 16:483. [PMID: 38675143 PMCID: PMC11053753 DOI: 10.3390/pharmaceutics16040483] [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: 02/19/2024] [Revised: 03/20/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Diabetes, characterized by elevated blood sugar levels, poses significant health and economic risks, correlating with complications like cardiovascular disease, kidney failure, and blindness. Dipeptidyl peptidase-4 (DPP-4), also referred to as T-cell activation antigen CD26 (EC 3.4.14.5.), plays a crucial role in glucose metabolism and immune function. Inhibiting DPP-4 was anticipated as a potential new therapy for diabetes. Therefore, identification of plant-based natural inhibitors of DPP-4 would help in eradicating diabetes worldwide. Here, for the identification of the potential natural inhibitors of DPP-4, we developed a phytochemicals library consisting of over 6000 phytochemicals detected in 81 medicinal plants that exhibited anti-diabetic potency. The library has been docked against the target proteins, where isorhamnetin, Benzyl 5-Amino-5-deoxy-2,3-O-isopropyl-alpha-D-mannofuranoside (DTXSID90724586), and 5-Oxo-7-[4-(trifluoromethyl) phenyl]-4H,6H,7H-[1,2]thiazolo[4,5-b]pyridine 3-carboxylic acid (CHEMBL3446108) showed binding affinities of -8.5, -8.3, and -8.3 kcal/mol, respectively. These compounds exhibiting strong interactions with DPP-4 active sites (Glu205, Glu206, Tyr547, Trp629, Ser630, Tyr662, His740) were identified. ADME/T and bioactivity predictions affirmed their pharmacological safety. Density functional theory calculations assessed stability and reactivity, while molecular dynamics simulations demonstrated persistent stability. Analyzing parameters like RMSD, RG, RMSF, SASA, H-bonds, MM-PBSA, and FEL confirmed stable protein-ligand compound formation. Principal component analysis provided structural variation insights. Our findings suggest that those compounds might be possible candidates for developing novel inhibitors targeting DPP-4 for treating diabetes.
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Affiliation(s)
- Alomgir Hossain
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (A.H.); (M.E.R.); (R.Z.); (M.N.M.)
| | - Md Ekhtiar Rahman
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (A.H.); (M.E.R.); (R.Z.); (M.N.M.)
| | - Md Omar Faruqe
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh;
| | - Ahmed Saif
- Department of Pharmacy, University of Rajshahi, Rajshahi 6205, Bangladesh;
| | - Suzzada Suhi
- Department of Zoology, University of Rajshahi, Rajshahi 6205, Bangladesh;
| | - Rashed Zaman
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (A.H.); (M.E.R.); (R.Z.); (M.N.M.)
| | - Abdurahman Hajinur Hirad
- Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia;
| | - Mohammad Nurul Matin
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (A.H.); (M.E.R.); (R.Z.); (M.N.M.)
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongsangbuk-do, Republic of Korea
| | - Muhammad Fazle Rabbee
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongsangbuk-do, Republic of Korea
| | - Kwang-Hyun Baek
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongsangbuk-do, Republic of Korea
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4
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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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5
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Weng G, Zhao H, Nie D, Zhang H, Liu L, Hou T, Kang Y. RediscMol: Benchmarking Molecular Generation Models in Biological Properties. J Med Chem 2024; 67:1533-1543. [PMID: 38181194 DOI: 10.1021/acs.jmedchem.3c02051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024]
Abstract
Deep learning-based molecular generative models have garnered emerging attention for their capability to generate molecules with novel structures and desired physicochemical properties. However, the evaluation of these models, particularly in a biological context, remains insufficient. To address the limitations of existing metrics and emulate practical application scenarios, we construct the RediscMol benchmark that comprises active molecules extracted from 5 kinase and 3 GPCR data sets. A set of rediscovery- and similarity-related metrics are introduced to assess the performance of 8 representative generative models (CharRNN, VAE, Reinvent, AAE, ORGAN, RNNAttn, TransVAE, and GraphAF). Our findings based on the RediscMol benchmark differ from those of previous evaluations. CharRNN, VAE, and Reinvent exhibit a greater ability to reproduce known active molecules, while RNNAttn, TransVAE, and GraphAF struggle in this aspect despite their notable performance on commonly used distribution-learning metrics. Our evaluation framework may provide valuable guidance for advancing generative models in real-world drug design scenarios.
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Affiliation(s)
- Gaoqi Weng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Huifeng Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Dou Nie
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen 518129, Guangdong, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityHangzhou 310058, Zhejiang, China
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6
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Guo W, Lv C, Guo M, Zhao Q, Yin X, Zhang L. Innovative applications of artificial intelligence in zoonotic disease management. SCIENCE IN ONE HEALTH 2023; 2:100045. [PMID: 39077042 PMCID: PMC11262289 DOI: 10.1016/j.soh.2023.100045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/22/2023] [Indexed: 07/31/2024]
Abstract
Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.
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Affiliation(s)
- Wenqiang Guo
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chenrui Lv
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Meng Guo
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450046, China
| | - Qiwei Zhao
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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7
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Raza A, Chohan TA, Buabeid M, Arafa ESA, Chohan TA, Fatima B, Sultana K, Ullah MS, Murtaza G. Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics. J Biomol Struct Dyn 2023; 41:9177-9192. [PMID: 36305195 DOI: 10.1080/07391102.2022.2136244] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/08/2022] [Indexed: 10/31/2022]
Abstract
Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ali Raza
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
| | - Talha Ali Chohan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
- Institute of Pharmaceutical Science, UVAS, Lahore, Pakistan
| | - Manal Buabeid
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
| | - El-Shaima A Arafa
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | | | - Batool Fatima
- Department of biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Kishwar Sultana
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
| | - Malik Saad Ullah
- Department of Pharmacy, Government College University, Faisalabad, Pakistan
| | - Ghulam Murtaza
- Department of Pharmacy, COMSATS University Islamabad, Lahore Campus, Pakistan
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8
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Vangala SR, Krishnan SR, Bung N, Srinivasan R, Roy A. pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules. J Chem Inf Model 2023; 63:5066-5076. [PMID: 37585609 DOI: 10.1021/acs.jcim.3c00689] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Generative artificial intelligence algorithms have shown to be successful in exploring large chemical spaces and designing novel and diverse molecules. There has been considerable interest in developing predictive models using artificial intelligence for drug-like properties, which can potentially reduce the late-stage attrition of drug candidates or predict the properties of novel AI-designed molecules. Concurrently, it is important to understand the contribution of functional groups toward these properties and modify them to obtain property-optimized lead compounds. As a result, there is an increasing interest in the development of explainable property prediction models. However, current explainable approaches are mostly atom-based, where, often, only a fraction of a fragment is shown to be significant. To address the above challenges, we have developed a novel domain-aware molecular fragmentation approach termed post-processing of BRICS (pBRICS), which can fragment small molecules into their functional groups. Multitask models were developed to predict various properties, including the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The fragment importance was explained using the gradient-weighted class activation mapping (Grad-CAM) approach. The method was validated on data sets of experimentally available matched molecular pairs (MMPs). The explanations from the model can be useful for medicinal chemists to identify the fragments responsible for poor drug-like properties and optimize the molecule. The explainability approach was also used to identify the reason behind false positive and false negative MMP predictions. Based on evidence from the existing literature and our analysis, some of these mispredictions were justified. We propose that the quantity, quality, and diversity of the training data will improve the accuracy of property prediction algorithms for novel molecules.
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Affiliation(s)
- Sarveswara Rao Vangala
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | | | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
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9
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Zhang W, Zhang K, Huang J. A Simple Way to Incorporate Target Structural Information in Molecular Generative Models. J Chem Inf Model 2023. [PMID: 37318828 DOI: 10.1021/acs.jcim.3c00293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. The method combines a message-passing neural network model that predicts docking scores with a generative neural network model as its reward function to navigate the chemical space searching for molecules that bind favorably with a specific target. A key feature of the method is the construction of target-specific molecular sets for training, designed to overcome potential transferability issues of surrogate docking models through a two-round training process. Consequently, this enables accurate guided exploration of the chemical space without reliance on the collection of prior knowledge about active and inactive compounds for the specific target. Tests on eight target proteins showed a 100-fold increase in hit generation compared to conventional docking calculations and the ability to generate molecules similar to approved drugs or known active ligands for specific targets without prior knowledge. This method provides a general and highly efficient solution for structure-based molecular generation.
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Affiliation(s)
- Wenyi Zhang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Kaiyue Zhang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Huang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
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10
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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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11
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Chen Y, Wang Z, Wang L, Wang J, Li P, Cao D, Zeng X, Ye X, Sakurai T. Deep generative model for drug design from protein target sequence. J Cheminform 2023; 15:38. [PMID: 36978179 PMCID: PMC10052801 DOI: 10.1186/s13321-023-00702-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 02/18/2023] [Indexed: 03/30/2023] Open
Abstract
Drug discovery for a protein target is a laborious and costly process. Deep learning (DL) methods have been applied to drug discovery and successfully generated novel molecular structures, and they can substantially reduce development time and costs. However, most of them rely on prior knowledge, either by drawing on the structure and properties of known molecules to generate similar candidate molecules or extracting information on the binding sites of protein pockets to obtain molecules that can bind to them. In this paper, DeepTarget, an end-to-end DL model, was proposed to generate novel molecules solely relying on the amino acid sequence of the target protein to reduce the heavy reliance on prior knowledge. DeepTarget includes three modules: Amino Acid Sequence Embedding (AASE), Structural Feature Inference (SFI), and Molecule Generation (MG). AASE generates embeddings from the amino acid sequence of the target protein. SFI inferences the potential structural features of the synthesized molecule, and MG seeks to construct the eventual molecule. The validity of the generated molecules was demonstrated by a benchmark platform of molecular generation models. The interaction between the generated molecules and the target proteins was also verified on the basis of two metrics, drug-target affinity and molecular docking. The results of the experiments indicated the efficacy of the model for direct molecule generation solely conditioned on amino acid sequence.
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Affiliation(s)
- Yangyang Chen
- Department of Computer Science, University of Tsukuba, Tsukuba, 3058577, Japan.
| | - Zixu Wang
- Department of Computer Science, University of Tsukuba, Tsukuba, 3058577, Japan
| | - Lei Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, China
| | - Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, 21983, Republic of Korea
- Bioinformatics and Molecular Design Research Center (BMDRC), Incheon, 21983, Republic of Korea
| | - Pengyong Li
- School of Computer Science and Technology, Xidian University, Xian, 710071, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, China.
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, People's Republic of China.
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, 3058577, Japan.
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba, 3058577, Japan
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12
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Das D, Chakrabarty B, Srinivasan R, Roy A. Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures. J Chem Inf Model 2023; 63:1882-1893. [PMID: 36971750 DOI: 10.1021/acs.jcim.2c01301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Drug-induced gene expression profiling provides a lot of useful information covering various aspects of drug discovery and development. Most importantly, this knowledge can be used to discover drugs' mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. Recent advances in accessibility of open-source drug-induced transcriptomic data along with the ability of deep learning algorithms to understand hidden patterns have opened opportunities for designing drug molecules based on desired gene expression signatures. In this study, we propose a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation), to generate novel drug-like molecules based on desired gene expression profiles. The model accepts desired gene expression profiles in a cell-specific manner as input and designs drug-like molecules which can elicit the required transcriptomic profile. The model was first tested against individual gene-knocked-out transcriptomic profiles, where the newly designed molecules showed high similarity with known inhibitors of the knocked-out target genes. The model was next applied on a triple negative breast cancer signature profile, where it could generate novel molecules, highly similar to known anti-breast cancer drugs. Overall, this work provides a generalized method, where the method first learned the molecular signature of a given cell due to a specific condition, and designs new small molecules with drug-like properties.
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13
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Choi J, Seo S, Choi S, Piao S, Park C, Ryu SJ, Kim BJ, Park S. ReBADD-SE: Multi-objective molecular optimisation using SELFIES fragment and off-policy self-critical sequence training. Comput Biol Med 2023; 157:106721. [PMID: 36913852 DOI: 10.1016/j.compbiomed.2023.106721] [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: 12/20/2022] [Revised: 02/11/2023] [Accepted: 02/26/2023] [Indexed: 03/02/2023]
Abstract
The discovery of drugs to selectively remove disease-related cells is challenging in computer-aided drug design. Many studies have proposed multi-objective molecular generation methods and demonstrated their superiority using the public benchmark dataset for kinase inhibitor generation tasks. However, the dataset does not contain many molecules that violate Lipinski's rule of five. Thus, it remains unclear whether existing methods are effective in generating molecules violating the rule, such as navitoclax. To address this, we analysed the limitations of existing methods and propose a multi-objective molecular generation method with a novel parsing algorithm for molecular string representation and a modified reinforcement learning method for the efficient training of multi-objective molecular optimisation. The proposed model had success rates of 84% in GSK3b+JNK3 inhibitor generation and 99% in Bcl-2 family inhibitor generation tasks.
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Affiliation(s)
- Jonghwan Choi
- Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea; UBLBio Corporation, Yeongtong-ro 237, Suwon, 16679, Gyeonggi-do, Republic of Korea.
| | - Sangmin Seo
- Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea; UBLBio Corporation, Yeongtong-ro 237, Suwon, 16679, Gyeonggi-do, Republic of Korea
| | - Seungyeon Choi
- Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea
| | - Shengmin Piao
- Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea
| | - Chihyun Park
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon-si, 24341, Kangwon-do, Republic of Korea; UBLBio Corporation, Yeongtong-ro 237, Suwon, 16679, Gyeonggi-do, Republic of Korea
| | - Sung Jin Ryu
- UBLBio Corporation, Yeongtong-ro 237, Suwon, 16679, Gyeonggi-do, Republic of Korea
| | - Byung Ju Kim
- UBLBio Corporation, Yeongtong-ro 237, Suwon, 16679, Gyeonggi-do, Republic of Korea
| | - Sanghyun Park
- Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea.
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14
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Zhang Y, Li S, Xing M, Yuan Q, He H, Sun S. Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning. ACS OMEGA 2023; 8:5464-5474. [PMID: 36816653 PMCID: PMC9933084 DOI: 10.1021/acsomega.2c06653] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 01/05/2023] [Indexed: 05/28/2023]
Abstract
In drug design, the design and manufacture of safe and effective compounds is a long-term, complex, and complicated process. Therefore, developing a new rapid and generalizable drug design method is of great value. This study aimed to propose a general model based on reinforcement learning combined with drug-target interaction, which could be used to design new molecules according to different protein targets. The method adopted recurrent neural network molecular modeling and took the drug-target affinity model as the reward function of optimal molecular generation. It did not need to know the three-dimensional structure and active sites of protein targets but only required the information of a one-dimensional amino acid sequence. This approach was demonstrated to produce drugs highly similar to marketed drugs and design molecules with a better binding energy.
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Affiliation(s)
- Yunjiang Zhang
- Beijing
Key Laboratory for Green Catalysis and Separation, The Faculty of
Environment and Life, Beijing University
of Technology, Beijing100124, PR China
| | - Shuyuan Li
- Beijing
Key Laboratory for Green Catalysis and Separation, The Faculty of
Environment and Life, Beijing University
of Technology, Beijing100124, PR China
| | - Miaojuan Xing
- Beijing
Key Laboratory for Green Catalysis and Separation, The Faculty of
Environment and Life, Beijing University
of Technology, Beijing100124, PR China
| | - Qing Yuan
- Department
of Chemistry and Chemical Engineering, Beijing
University of Technology, Beijing100124, China
| | - Hong He
- Beijing
Key Laboratory for Green Catalysis and Separation, The Faculty of
Environment and Life, Beijing University
of Technology, Beijing100124, PR China
| | - Shaorui Sun
- Beijing
Key Laboratory for Green Catalysis and Separation, The Faculty of
Environment and Life, Beijing University
of Technology, Beijing100124, PR China
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15
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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16
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Chen L, Yu L, Gao L. Potent antibiotic design via guided search from antibacterial activity evaluations. Bioinformatics 2023; 39:7008322. [PMID: 36707990 PMCID: PMC9897189 DOI: 10.1093/bioinformatics/btad059] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/14/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. RESULTS By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. AVAILABILITY AND IMPLEMENTATION Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lu Chen
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
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17
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PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning. Int J Mol Sci 2023; 24:ijms24021146. [PMID: 36674658 PMCID: PMC9865828 DOI: 10.3390/ijms24021146] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/29/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed. This method is useful when the dataset for target-specific ligands is limited. Deep learning methods can extract and learn features (representations) in a data-driven way with little or no human participation. Generative pretraining (GPT) was used to extract the contextual features of the molecule. Three different protein-encoding methods were used to extract the physicochemical properties and amino acid information of the target protein. Protein-encoding and molecular sequence information are combined to guide molecule generation. Transfer learning was used to fine-tune the pretrained model to generate molecules with better binding ability to the target protein. The model was validated using three different targets. The docking results show that our model is capable of generating new molecules with higher docking scores for the target proteins.
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18
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Krishnan SR, Bung N, Padhi S, Bulusu G, Misra P, Pal M, Oruganti S, Srinivasan R, Roy A. De novo design of anti-tuberculosis agents using a structure-based deep learning method. J Mol Graph Model 2023; 118:108361. [PMID: 36257148 DOI: 10.1016/j.jmgm.2022.108361] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/10/2022] [Accepted: 10/07/2022] [Indexed: 11/28/2022]
Abstract
Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Siladitya Padhi
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Gopalakrishnan Bulusu
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India; Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Parimal Misra
- Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Manojit Pal
- Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Srinivas Oruganti
- Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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19
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Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics 2022; 15:pharmaceutics15010049. [PMID: 36678678 PMCID: PMC9867171 DOI: 10.3390/pharmaceutics15010049] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
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Affiliation(s)
- Yiqun Chang
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Bryson A. Hawkins
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Jonathan J. Du
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Paul W. Groundwater
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - David E. Hibbs
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Felcia Lai
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
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20
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Tan Y, Dai L, Huang W, Guo Y, Zheng S, Lei J, Chen H, Yang Y. DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design. J Chem Inf Model 2022; 62:5907-5917. [PMID: 36404642 DOI: 10.1021/acs.jcim.2c00982] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.
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Affiliation(s)
- Youhai Tan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
| | - Lingxue Dai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
| | - Weifeng Huang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Yinfeng Guo
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Shuangjia Zheng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China.,Galixir Technologies, Beijing100083, China
| | - Jinping Lei
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Hongming Chen
- Guangzhou Laboratory, No. 9 XinDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou510005, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
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21
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Ramaswamy Krishnan S, Roy A, Michael Gromiha M. R-SIM: A database of binding affinities for RNA-small molecule interactions. J Mol Biol 2022:167914. [DOI: 10.1016/j.jmb.2022.167914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/28/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
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22
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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23
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Thomas M, O'Boyle NM, Bender A, de Graaf C. Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation. J Cheminform 2022; 14:68. [PMID: 36192789 PMCID: PMC9531503 DOI: 10.1186/s13321-022-00646-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 09/23/2022] [Indexed: 11/10/2022] Open
Abstract
A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 105 molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 and 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~ 1.5-fold and sample-efficiency is improved ~ 45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. We find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Lastly, we show improved performance not only on recurrent neural networks but also on a reinforcement learning stabilized transformer architecture. Overall, we show that Augmented Hill-Climb improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale.
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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, Sosei Heptares, 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, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
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24
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An interpretable machine learning model for selectivity of small molecules against homologous protein family. Future Med Chem 2022; 14:1441-1453. [PMID: 36169035 DOI: 10.4155/fmc-2022-0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinase and dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boosting and deep neural network models outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.
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25
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Jia XN, Wang WJ, Yin B, Zhou LJ, Zhen YQ, Zhang L, Zhou XL, Song HN, Tang Y, Gao F. Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity. ACS OMEGA 2022; 7:28334-28341. [PMID: 35990425 PMCID: PMC9386835 DOI: 10.1021/acsomega.2c02854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
Natural microtubule inhibitors, such as paclitaxel and ixabepilone, are key sources of novel medications, which have a considerable influence on anti-tumor chemotherapy. Natural product chemists have been encouraged to create novel methodologies for screening the new generation of microtubule inhibitors from the enormous natural product library. There have been major advancements in the use of artificial intelligence in medication discovery recently. Deep learning algorithms, in particular, have shown promise in terms of swiftly screening effective leads from huge compound libraries and producing novel compounds with desirable features. We used a deep neural network to search for potent β-microtubule inhibitors in natural goods. Eleutherobin, bruceine D (BD), and phorbol 12-myristate 13-acetate (PMA) are three highly effective natural compounds that have been found as β-microtubule inhibitors. In conclusion, this paper describes the use of deep learning to screen for effective β-microtubule inhibitors. This research also demonstrates the promising possibility of employing deep learning to develop drugs from natural products for a wider range of disorders.
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Affiliation(s)
- Xiao-Nan Jia
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Wei-Jia Wang
- School
of Computer Science and Engineering, University
of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Bo Yin
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Lin-Jing Zhou
- School
of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Yong-Qi Zhen
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Lan Zhang
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Xian-Li Zhou
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Hai-Ning Song
- Department
of Pharmacy, The Third People’s Hospital of Chengdu and College
of Medicine, Southwest Jiaotong University, Chengdu 610031, PR China
| | - Yong Tang
- School
of Computer Science and Engineering, University
of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Feng Gao
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
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26
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Yang L, Yang G, Bing Z, Tian Y, Huang L, Niu Y, Yang L. Accelerating the discovery of anticancer peptides targeting lung and breast cancers with the Wasserstein autoencoder model and PSO algorithm. Brief Bioinform 2022; 23:6658854. [PMID: 35945135 DOI: 10.1093/bib/bbac320] [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: 04/24/2022] [Revised: 06/14/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
In the development of targeted drugs, anticancer peptides (ACPs) have attracted great attention because of their high selectivity, low toxicity and minimal non-specificity. In this work, we report a framework of ACPs generation, which combines Wasserstein autoencoder (WAE) generative model and Particle Swarm Optimization (PSO) forward search algorithm guided by attribute predictive model to generate ACPs with desired properties. It is well known that generative models based on Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN) are difficult to be used for de novo design due to the problems of posterior collapse and difficult convergence of training. Our WAE-based generative model trains more successfully (lower perplexity and reconstruction loss) than both VAE and GAN-based generative models, and the semantic connections in the latent space of WAE accelerate the process of forward controlled generation of PSO, while VAE fails to capture this feature. Finally, we validated our pipeline on breast cancer targets (HIF-1) and lung cancer targets (VEGR, ErbB2), respectively. By peptide-protein docking, we found candidate compounds with the same binding sites as the peptides carried in the crystal structure but with higher binding affinity and novel structures, which may be potent antagonists that interfere with these target-mediated signaling.
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Affiliation(s)
- Lijuan Yang
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,School of Physics and Technology, Lanzhou University, Lanzhou 730000, China.,School of Physics, University of Chinese Academy of Science, Beijing 100049, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
| | - Guanghui Yang
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
| | - Zhitong Bing
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
| | - Yuan Tian
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Liang Huang
- School of Physics and Technology, Lanzhou University, Lanzhou 730000, China
| | - Yuzhen Niu
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo 255000, China
| | - Lei Yang
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
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27
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Bung N, Krishnan SR, Roy A. An In Silico Explainable Multiparameter Optimization Approach for De Novo Drug Design against Proteins from the Central Nervous System. J Chem Inf Model 2022; 62:2685-2695. [PMID: 35581002 DOI: 10.1021/acs.jcim.2c00462] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The aim of drug design and development is to produce a drug that can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multistep process where different parameters such as activity and physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method that can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was applied against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which act as an additional challenge for CNS drug delivery. The contribution of each parameter toward molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design similar inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques.
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Affiliation(s)
- Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | | | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
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28
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Bai Q, Liu S, Tian Y, Xu T, Banegas‐Luna AJ, Pérez‐Sánchez H, Huang J, Liu H, Yao X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1581] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University Lanzhou Gansu China
| | - Shuo Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Yanan Tian
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Tingyang Xu
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Antonio Jesús Banegas‐Luna
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Horacio Pérez‐Sánchez
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Junzhou Huang
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Huanxiang Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering Lanzhou University Lanzhou Gansu China
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29
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Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments. Pharmaceuticals (Basel) 2022; 15:ph15020236. [PMID: 35215348 PMCID: PMC8875555 DOI: 10.3390/ph15020236] [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: 12/17/2021] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.
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30
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Wang J, Zhang Y, Nie W, Luo Y, Deng L. Computational anti-COVID-19 drug design: progress and challenges. Brief Bioinform 2022; 23:bbab484. [PMID: 34850817 PMCID: PMC8690229 DOI: 10.1093/bib/bbab484] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/14/2022] Open
Abstract
Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.
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Affiliation(s)
- Jinxian Wang
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, 150001, Harbin, China
| | - Wenjuan Nie
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
| | - Yi Luo
- School of Science, The University of Auckland,Auckland 1010, Auckland, New Zealand
| | - Lei Deng
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
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31
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Li B, Sun H, Shu H, Wang X. Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification. ACS OMEGA 2022; 7:168-175. [PMID: 35036688 PMCID: PMC8756567 DOI: 10.1021/acsomega.1c04287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
The rapidly developing artificial intelligence (AI) requires revolutionary computing architectures to break the energy efficiency bottleneck caused by the traditional von Neumann computing architecture. In addition, the emerging brain-machine interface also requires computational circuitry that can conduct large parallel computational tasks with low energy cost and good biocompatibility. Neuromorphic computing, a novel computational architecture emulating human brains, has drawn significant interest for the aforementioned applications due to its low energy cost, capability to parallelly process large-scale data, and biocompatibility. Most efforts in the domain of neuromorphic computing focus on addressing traditional AI problems, such as handwritten digit recognition and file classification. Here, we demonstrate for the first time that current neuromorphic computing techniques can be used to solve key machine learning questions in cheminformatics. We predict the band gaps of small-molecule organic semiconductors and classify chemical reaction types with a simulated neuromorphic circuitry. Our work can potentially guide the design and fabrication of elementary devices and circuitry for neuromorphic computing specialized for chemical purposes.
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Affiliation(s)
- Baochen Li
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Haibin Sun
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Haonian Shu
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Xiaoxue Wang
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
- The
Ohio State University Sustainability Institute, 3018 Smith Lab, 174 W. 18th Avenue, Columbus, Ohio 43210, United States
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32
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Sridharan B, Goel M, Priyakumar UD. Modern Machine Learning for Tackling Inverse Problems in Chemistry: Molecular Design to Realization. Chem Commun (Camb) 2022; 58:5316-5331. [DOI: 10.1039/d1cc07035e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In the pursuit of finding molecules with desired properties, chemists have traditionally relied...
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33
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Goel M, Raghunathan S, Laghuvarapu S, Priyakumar UD. MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards. J Chem Inf Model 2021; 61:5815-5826. [PMID: 34866384 DOI: 10.1021/acs.jcim.1c01341] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches such as high-throughput virtual screening require extensive combing through existing data sets in the hope of finding possible matches. In this study, we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target and other desirable properties for druglike molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate druglike molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, quantitative estimate of drug likeliness, topological polar surface area, and hydration free energy along with the binding affinity. For multiobjective optimization, we have devised a novel strategy in which the property being used to calculate the reward is changed periodically. In comparison to the conventional approach of taking a weighted sum of all rewards, this strategy shows an enhanced ability to generate a significantly higher number of molecules with desirable properties.
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Affiliation(s)
- Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India.,École Centrale School of Engineering, Mahindra University, Hyderabad 500 043, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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34
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Yang L, Yang G, Bing Z, Tian Y, Niu Y, Huang L, Yang L. Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors. ACS OMEGA 2021; 6:33864-33873. [PMID: 34926933 PMCID: PMC8674994 DOI: 10.1021/acsomega.1c05145] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/08/2021] [Indexed: 06/14/2023]
Abstract
The de novo drug design based on SMILES format is a typical sequence-processing problem. Previous methods based on recurrent neural network (RNN) exhibit limitation in capturing long-range dependency, resulting in a high invalid percentage in generated molecules. Recent studies have shown the potential of Transformer architecture to increase the capacity of handling sequence data. In this work, the encoder module in the Transformer is used to build a generative model. First, we train a Transformer-encoder-based generative model to learn the grammatical rules of known drug molecules and a predictive model to predict the activity of the molecules. Subsequently, transfer learning and reinforcement learning were used to fine-tune and optimize the generative model, respectively, to design new molecules with desirable activity. Compared with previous RNN-based methods, our method has improved the percentage of generating chemically valid molecules (from 95.6 to 98.2%), the structural diversity of the generated molecules, and the feasibility of molecular synthesis. The pipeline is validated by designing inhibitors against the human BRAF protein. Molecular docking and binding mode analysis showed that our method can generate small molecules with higher activity than those carrying ligands in the crystal structure and have similar interaction sites with these ligands, which can provide new ideas and suggestions for pharmaceutical chemists.
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Affiliation(s)
- Lijuan Yang
- Institute
of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
- School
of Physics and Technology, Lanzhou University, Lanzhou 730000, China
- School
of Physics, University of Chinese Academy
of Sciences, Beijing 100049, China
- Advanced
Energy Science and Technology, Guangdong
Laboratory, Huizhou 516000, China
| | - Guanghui Yang
- Institute
of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
- Advanced
Energy Science and Technology, Guangdong
Laboratory, Huizhou 516000, China
| | - Zhitong Bing
- Institute
of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
- Advanced
Energy Science and Technology, Guangdong
Laboratory, Huizhou 516000, China
| | - Yuan Tian
- Institute
of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
- School
of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Yuzhen Niu
- Shandong
Provincial Research Center for Bioinformatic Engineering and Technique,
School of Life Sciences, Shandong University
of Technology, Zibo 255000, China
| | - Liang Huang
- School
of Physics and Technology, Lanzhou University, Lanzhou 730000, China
| | - Lei Yang
- Institute
of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
- Advanced
Energy Science and Technology, Guangdong
Laboratory, Huizhou 516000, China
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35
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Krishnan SR, Bung N, Vangala SR, Srinivasan R, Bulusu G, Roy A. De Novo Structure-Based Drug Design Using Deep Learning. J Chem Inf Model 2021; 62:5100-5109. [PMID: 34792338 DOI: 10.1021/acs.jcim.1c01319] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In recent years, deep learning-based methods have emerged as promising tools for de novo drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties. Although there have been attempts to develop alternative ways to design target-specific ligand data sets, availability of such data sets remains a challenge while designing molecules against novel target proteins. In this work, we propose a deep learning-based method, where the knowledge of the active site structure of the target protein is sufficient to design new molecules. First, a graph attention model was used to learn the structure and features of the amino acids in the active site of proteins that are experimentally known to form protein-ligand complexes. Next, the learned active site features were used along with a pretrained generative model for conditional generation of new molecules. A bioactivity prediction model was then used in a reinforcement learning framework to optimize the conditional generative model. We validated our method against two well-studied proteins, Janus kinase 2 (JAK2) and dopamine receptor D2 (DRD2), where we produce molecules similar to the known inhibitors. The graph attention model could identify the probable key active site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Sarveswara Rao Vangala
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Gopalakrishnan Bulusu
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
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36
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Choi J, Lee J. V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization. Int J Mol Sci 2021; 22:11635. [PMID: 34769065 PMCID: PMC8584000 DOI: 10.3390/ijms222111635] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/13/2021] [Accepted: 10/24/2021] [Indexed: 02/06/2023] Open
Abstract
We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.
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Affiliation(s)
- Jieun Choi
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, Chuncheon 24341, Korea;
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, Chuncheon 24341, Korea;
- Arontier Co., Seoul 06735, Korea
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37
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Fialková V, Zhao J, Papadopoulos K, Engkvist O, Bjerrum EJ, Kogej T, Patronov A. LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design. J Chem Inf Model 2021; 62:2046-2063. [PMID: 34460269 DOI: 10.1021/acs.jcim.1c00469] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called LibINVENT. It is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. LibINVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimization in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possess desirable properties, and can also be synthesized under the same or similar conditions. The LibINVENT code is freely available in our public repository at https://github.com/MolecularAI/Lib-INVENT. The code necessary for data preprocessing is further available at: https://github.com/MolecularAI/Lib-INVENT-dataset.
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Affiliation(s)
- Vendy Fialková
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 43183, Sweden
| | - Jiaxi Zhao
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 43183, Sweden.,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala 75237, Sweden
| | - Kostas Papadopoulos
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 43183, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 43183, Sweden.,Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 41756, Sweden
| | | | - Thierry Kogej
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 43183, Sweden
| | - Atanas Patronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 43183, Sweden
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38
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Yu L, Su Y, Liu Y, Zeng X. Review of unsupervised pretraining strategies for molecules representation. Brief Funct Genomics 2021; 20:323-332. [PMID: 34342611 DOI: 10.1093/bfgp/elab036] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/14/2022] Open
Abstract
In recent years, the computer-assisted techniques make a great progress in the field of drug discovery. And, yet, the problem of limited labeled data problem is still challenging and also restricts the performance of these techniques in specific tasks, such as molecular property prediction, compound-protein interaction and de novo molecular generation. One effective solution is to utilize the experience and knowledge gained from other tasks to cope with related pursuits. Unsupervised pretraining is promising, due to its capability of leveraging a vast number of unlabeled molecules and acquiring a more informative molecular representation for the downstream tasks. In particular, models trained on large-scale unlabeled molecules can capture generalizable features, and this ability can be employed to improve the performance of specific downstream tasks. Many relevant pretraining works have been recently proposed. Here, we provide an overview of molecular unsupervised pretraining and related applications in drug discovery. Challenges and possible solutions are also summarized.
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39
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Gupta R, Mittal A, Agrawal V, Gupta S, Gupta K, Jain RR, Garg P, Mohanty SK, Sogani R, Chhabra HS, Gautam V, Mishra T, Sengupta D, Ahuja G. OdoriFy: A conglomerate of artificial intelligence-driven prediction engines for olfactory decoding. J Biol Chem 2021; 297:100956. [PMID: 34265305 PMCID: PMC8342790 DOI: 10.1016/j.jbc.2021.100956] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/24/2021] [Accepted: 07/09/2021] [Indexed: 12/01/2022] Open
Abstract
The molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network-based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant-OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.
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Affiliation(s)
- Ria Gupta
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Vishesh Agrawal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Sushant Gupta
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Krishan Gupta
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Rishi Raj Jain
- Department of Computer Science and Design, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Prakriti Garg
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Riya Sogani
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Harshit Singh Chhabra
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Vishakha Gautam
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India
| | - Tripti Mishra
- Pathfinder Research and Training Foundation, Greater Noida, Uttar Pradesh, India
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India; Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India; Centre for Artificial Intelligence, Indraprastha Institute of Information Technology, New Delhi, India; Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India.
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Born J, Manica M, Oskooei A, Cadow J, Markert G, Rodríguez Martínez M. PaccMann RL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning. iScience 2021; 24:102269. [PMID: 33851095 PMCID: PMC8022157 DOI: 10.1016/j.isci.2021.102269] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 01/11/2021] [Accepted: 03/01/2021] [Indexed: 02/07/2023] Open
Abstract
With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the cellular environment of target diseases. Bridging systems biology and drug design, we present a reinforcement learning method for de novo molecular design from gene expression profiles. We construct a hybrid Variational Autoencoder that tailors molecules to target-specific transcriptomic profiles, using an anticancer drug sensitivity prediction model (PaccMann) as reward function. Without incorporating information about anticancer drugs, the molecule generation is biased toward compounds with high predicted efficacy against cell lines or cancer types. The generation can be further refined by subsidiary constraints such as toxicity. Our cancer-type-specific candidate drugs are similar to cancer drugs in drug-likeness, synthesizability, and solubility and frequently exhibit the highest structural similarity to compounds with known efficacy against these cancer types.
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Affiliation(s)
- Jannis Born
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | | | - Ali Oskooei
- IBM Research Europe, 8803 Rüschlikon, Switzerland
| | - Joris Cadow
- IBM Research Europe, 8803 Rüschlikon, Switzerland
| | - Greta Markert
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zürich, Switzerland
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Born J, Manica M, Cadow J, Markert G, Mill NA, Filipavicius M, Janakarajan N, Cardinale A, Laino T, Rodríguez Martínez M. Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abe808] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Abstract
Bridging systems biology and drug design, we propose a deep learning framework for de novo discovery of molecules tailored to bind with given protein targets. Our methodology is exemplified by the task of designing antiviral candidates to target SARS-CoV-2 related proteins. Crucially, our framework does not require fine-tuning for specific proteins but is demonstrated to generalize in proposing ligands with high predicted binding affinities against unseen targets. Coupling our framework with the automatic retrosynthesis prediction of IBM RXN for Chemistry, we demonstrate the feasibility of swift chemical synthesis of molecules with potential antiviral properties that were designed against a specific protein target. In particular, we synthesize an antiviral candidate designed against the host protein angiotensin converting enzyme 2 (ACE2); a surface receptor on human respiratory epithelial cells that facilitates SARS-CoV-2 cell entry through its spike glycoprotein.
This is achieved as follows. First, we train a multimodal ligand–protein binding affinity model on predicting affinities of bioactive compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a conditional molecular generator that consists of two variational autoencoders (VAE), our framework steers the generation toward regions of the chemical space with high-reward molecules. Specifically, we explore a challenging setting of generating ligands against unseen protein targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related target proteins. Using deep reinforcement learning, it is demonstrated that in 35 out of 41 cases, the generation is biased towards sampling binding ligands, with an average increase of 83% comparing to an unbiased VAE. The generated molecules exhibit favorable properties in terms of target binding affinity, selectivity and drug-likeness. We use molecular retrosynthetic models to provide a synthetic accessibility assessment of the best generated hit molecules. Finally, with this end-to-end framework, we synthesize 3-Bromobenzylamine, a potential inhibitor of the host ACE2 protein, solely based on the recommendations of a molecular retrosynthesis model and a synthesis protocol prediction model. We hope that our framework can contribute towards swift discovery of de novo molecules with desired pharmacological properties.
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