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Ye G. De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning. J Comput Aided Mol Des 2024; 38:20. [PMID: 38647700 PMCID: PMC11035455 DOI: 10.1007/s10822-024-00559-z] [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: 12/08/2023] [Accepted: 03/22/2024] [Indexed: 04/25/2024]
Abstract
In recent years, generative machine learning algorithms have been successful in designing innovative drug-like molecules. SMILES is a sequence-like language used in most effective drug design models. Due to data's sequential structure, models such as recurrent neural networks and transformers can design pharmacological compounds with optimized efficacy. Large language models have advanced recently, but their implications on drug design have not yet been explored. Although one study successfully pre-trained a large chemistry model (LCM), its application to specific tasks in drug discovery is unknown. In this study, the drug design task is modeled as a causal language modeling problem. Thus, the procedure of reward modeling, supervised fine-tuning, and proximal policy optimization was used to transfer the LCM to drug design, similar to Open AI's ChatGPT and InstructGPT procedures. By combining the SMILES sequence with chemical descriptors, the novel efficacy evaluation model exceeded its performance compared to previous studies. After proximal policy optimization, the drug design model generated molecules with 99.2% having efficacy pIC50 > 7 towards the amyloid precursor protein, with 100% of the generated molecules being valid and novel. This demonstrated the applicability of LCMs in drug discovery, with benefits including less data consumption while fine-tuning. The applicability of LCMs to drug discovery opens the door for larger studies involving reinforcement-learning with human feedback, where chemists provide feedback to LCMs and generate higher-quality molecules. LCMs' ability to design similar molecules from datasets paves the way for more accessible, non-patented alternatives to drug molecules.
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Affiliation(s)
- Gavin Ye
- Columbia Grammar & Preparatory School, New York, NY, USA.
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2
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Agu PC, Obulose CN. Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications. Drug Dev Res 2024; 85:e22159. [PMID: 38375772 DOI: 10.1002/ddr.22159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024]
Abstract
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.
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Affiliation(s)
- Peter Chinedu Agu
- Department of Biochemistry, College of Science, Evangel University, Akaeze, Ebonyi State, Nigeria
| | - Chidiebere Nwiboko Obulose
- Department of Computer Sciences, Our Savior Institute of Science, Agriculture, and Technology (OSISATECH Polytechnic), Enugu, Nigeria
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3
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Seo S, Lee JW. Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design). Methods Mol Biol 2024; 2714:295-305. [PMID: 37676605 DOI: 10.1007/978-1-0716-3441-7_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches.
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Affiliation(s)
- Seongmin Seo
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Jai Woo Lee
- Department of Big Data Science, College of Public Policy, Korea University, Sejong, Republic of Korea.
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4
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Chen L, Shen Q, Lou J. Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration. BMC Bioinformatics 2023; 24:173. [PMID: 37101113 PMCID: PMC10132416 DOI: 10.1186/s12859-023-05286-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 04/13/2023] [Indexed: 04/28/2023] Open
Abstract
The flourishment of machine learning and deep learning methods has boosted the development of cheminformatics, especially regarding the application of drug discovery and new material exploration. Lower time and space expenses make it possible for scientists to search the enormous chemical space. Recently, some work combined reinforcement learning strategies with recurrent neural network (RNN)-based models to optimize the property of generated small molecules, which notably improved a batch of critical factors for these candidates. However, a common problem among these RNN-based methods is that several generated molecules have difficulty in synthesizing despite owning higher desired properties such as binding affinity. However, RNN-based framework better reproduces the molecule distribution among the training set than other categories of models during molecule exploration tasks. Thus, to optimize the whole exploration process and make it contribute to the optimization of specified molecules, we devised a light-weighted pipeline called Magicmol; this pipeline has a re-mastered RNN network and utilize SELFIES presentation instead of SMILES. Our backbone model achieved extraordinary performance while reducing the training cost; moreover, we devised reward truncate strategies to eliminate the model collapse problem. Additionally, adopting SELFIES presentation made it possible to combine STONED-SELFIES as a post-processing procedure for specified molecule optimization and quick chemical space exploration.
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Affiliation(s)
- Lin Chen
- Yangtze Delta Region (Huzhou) Institute of Intelligent Transportation, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University, Huzhou, China
| | - Qing Shen
- Yangtze Delta Region (Huzhou) Institute of Intelligent Transportation, Huzhou University, Huzhou, China
- School of Electronic Information, Huzhou College, Huzhou, China
| | - Jungang Lou
- Yangtze Delta Region (Huzhou) Institute of Intelligent Transportation, Huzhou University, Huzhou, China.
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University, Huzhou, China.
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5
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Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 31:691-702. [PMID: 36923950 PMCID: PMC10009646 DOI: 10.1016/j.omtn.2023.02.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.
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Affiliation(s)
- Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xuesong Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Sanyin Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shilin Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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6
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Bhadwal AS, Kumar K, Kumar N. GenSMILES: An enhanced validity conscious representation for inverse design of molecules. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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7
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Fromer JC, Coley CW. Computer-aided multi-objective optimization in small molecule discovery. PATTERNS (NEW YORK, N.Y.) 2023; 4:100678. [PMID: 36873904 PMCID: PMC9982302 DOI: 10.1016/j.patter.2023.100678] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which imposes assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discovery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a relatively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportunities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi-objective de novo design.
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Affiliation(s)
- Jenna C Fromer
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Connor W Coley
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA.,Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
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8
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Vemula D, Jayasurya P, Sushmitha V, Kumar YN, Bhandari V. CADD, AI and ML in drug discovery: A comprehensive review. Eur J Pharm Sci 2023; 181:106324. [PMID: 36347444 DOI: 10.1016/j.ejps.2022.106324] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 11/03/2022] [Indexed: 11/06/2022]
Abstract
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.
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Affiliation(s)
- Divya Vemula
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | - Perka Jayasurya
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | - Varthiya Sushmitha
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | | | - Vasundhra Bhandari
- National Institute of Pharmaceutical Education and Research- Hyderabad, India.
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9
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Dorahy G, Chen JZ, Balle T. Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs. Molecules 2023; 28:molecules28031324. [PMID: 36770990 PMCID: PMC9921936 DOI: 10.3390/molecules28031324] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.
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Affiliation(s)
- Georgia Dorahy
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Jake Zheng Chen
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Thomas Balle
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
- Correspondence:
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10
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Molecular Property Prediction of Modified Gedunin Using Machine Learning. Molecules 2023; 28:molecules28031125. [PMID: 36770791 PMCID: PMC9921289 DOI: 10.3390/molecules28031125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/25/2023] Open
Abstract
Images of molecules are often utilized in education and synthetic exploration to predict molecular characteristics. Deep learning (DL) has also had an influence on drug research, such as the interpretation of cellular images as well as the development of innovative methods for the synthesis of organic molecules. Although research in these areas has been significant, a comprehensive review of DL applications in drug development would be beyond the scope of a single Account. In this study, we will concentrate on a single major area where DL has influenced molecular design: the prediction of molecular properties of modified gedunin using machine learning (ML). AI and ML technologies are critical in drug research and development. In these other words, deep learning (DL) algorithms and artificial neural networks (ANN) have changed the field. In short, advances in AI and ML present a good potential for rational drug design and exploration, which will ultimately benefit humanity. In this paper, long short-term memory (LSTM) was used to convert a modified gedunin SMILE into a molecular image. The 2D molecular representations and their immediately visible highlights should then provide adequate data to predict the subordinate characteristics of atom design. We aim to find the properties of modified gedunin using K-means clustering; RNN-like ML tools. To support this postulation, neural network (NN) clustering based on the AI picture is used and evaluated in this study. The novel chemical developed via profound learning has long been predicted on characteristics. As a result, LSTM with RNNs allow us to predict the properties of molecules of modified gedunin. The total accuracy of the suggested model is 98.68%. The accuracy of the molecular property prediction of modified gedunin research is promising enough to evaluate extrapolation and generalization. The model suggested in this research requires just seconds or minutes to calculate, making it faster as well as more effective than existing techniques. In short, ML can be a useful tool for predicting the properties of modified gedunin molecules.
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Sundin I, Voronov A, Xiao H, Papadopoulos K, Bjerrum EJ, Heinonen M, Patronov A, Kaski S, Engkvist O. Human-in-the-loop assisted de novo molecular design. J Cheminform 2022; 14:86. [PMID: 36578043 PMCID: PMC9795720 DOI: 10.1186/s13321-022-00667-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/03/2022] [Indexed: 12/29/2022] Open
Abstract
A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integrate human feedback in the exploration of the chemical space to optimize molecules. A human drug designer still needs to design the goal, expressed as a scoring function for the molecules that captures the designer's implicit knowledge about the optimization task. Little support for this task exists and, consequently, a chemist usually resorts to iteratively building the objective function of multi-parameter optimization (MPO) in de novo design. We propose a principled approach to use human-in-the-loop machine learning to help the chemist to adapt the MPO scoring function to better match their goal. An advantage is that the method can learn the scoring function directly from the user's feedback while they browse the output of the molecule generator, instead of the current manual tuning of the scoring function with trial and error. The proposed method uses a probabilistic model that captures the user's idea and uncertainty about the scoring function, and it uses active learning to interact with the user. We present two case studies for this: In the first use-case, the parameters of an MPO are learned, and in the second use-case a non-parametric component of the scoring function to capture human domain knowledge is developed. The results show the effectiveness of the methods in two simulated example cases with an oracle, achieving significant improvement in less than 200 feedback queries, for the goals of a high QED score and identifying potent molecules for the DRD2 receptor, respectively. We further demonstrate the performance gains with a medicinal chemist interacting with the system.
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Affiliation(s)
- Iiris Sundin
- grid.5373.20000000108389418Department of Computer Science, Aalto University, Espoo, Finland
| | - Alexey Voronov
- grid.418151.80000 0001 1519 6403Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Haoping Xiao
- grid.5373.20000000108389418Department of Computer Science, Aalto University, Espoo, Finland
| | - Kostas Papadopoulos
- grid.418151.80000 0001 1519 6403Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden ,Present Address: Odyssey Therapeutics, Cambridge, MA USA
| | - Esben Jannik Bjerrum
- grid.418151.80000 0001 1519 6403Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden ,Present Address: Odyssey Therapeutics, Cambridge, MA USA
| | - Markus Heinonen
- grid.5373.20000000108389418Department of Computer Science, Aalto University, Espoo, Finland
| | - Atanas Patronov
- grid.418151.80000 0001 1519 6403Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden ,Present Address: Odyssey Therapeutics, Cambridge, MA USA
| | - Samuel Kaski
- grid.5373.20000000108389418Department of Computer Science, Aalto University, Espoo, Finland ,grid.5379.80000000121662407Department of Computer Science, University of Manchester, Manchester, UK
| | - Ola Engkvist
- grid.418151.80000 0001 1519 6403Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden ,grid.5371.00000 0001 0775 6028Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
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12
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Lin Y, Zhang Y, Wang D, Yang B, Shen YQ. Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2022; 107:154481. [PMID: 36215788 DOI: 10.1016/j.phymed.2022.154481] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 09/14/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs. PURPOSE This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development. STUDY DESIGN We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research. METHODS The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM. RESULT Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery. CONCLUSIONS The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.
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Affiliation(s)
- Yumeng Lin
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - You Zhang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Dongyang Wang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bowen Yang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Ying-Qiang Shen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
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13
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Xu T, Wang M, Liu X, Feng D, Zhu Y, Fan Z, Rao S, Lu J. A Scaffold-based Deep Generative Model Considering Molecular Stereochemical Information. Mol Inform 2022; 41:e2200088. [PMID: 36031563 DOI: 10.1002/minf.202200088] [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: 12/13/2022]
Abstract
Designing molecules with specific scaffolds can facilitate the discovery and optimization of lead compounds. Some scaffold-based molecular generation models have been developed using deep-learning methods based on specific scaffolds, although incorporating scaffold generalization is expected to achieve scaffold hopping. Moreover, most of the existing models focus on the 2D shape of the scaffold and overlook the stereochemical properties of the compound, especially for natural products. In this study, we optimized the scaffold-based molecular generation model designed by Lim et al. (Chemical Science 2020, 11, 1153-1164). Real-time ultrafast shape recognition with pharmacophore constraints (USRCAT) was introduced into the model to search for molecules similar to the 3D conformation and pharmacophore of the input scaffold sourced from the training set; the searched molecules were then used as new scaffolds to execute scaffold hopping. The optimized model could generate new molecules with the same chirality as the input scaffold. Furthermore, the probability distribution of the molecular structure and various physicochemical properties were analyzed to evaluate the model's generation capability. We thus believe that the optimized model can provide a basis for medicinal chemists to explore a wider chemical space toward optimization of the lead compounds and to screen the virtual compound library.
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Affiliation(s)
- Tianxu Xu
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
| | - Minjun Wang
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
| | - Xiaoqian Liu
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
| | - Dawei Feng
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
| | - Yanjuan Zhu
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
| | - Zhe Fan
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
| | - Shurong Rao
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
| | - Jing Lu
- Department, Institution:Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai, 264005, China
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14
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Yoshizawa T, Ishida S, Sato T, Ohta M, Honma T, Terayama K. Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search. J Chem Inf Model 2022; 62:5351-5360. [DOI: 10.1021/acs.jcim.2c00787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Tatsuya Yoshizawa
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Shoichi Ishida
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Tomohiro Sato
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Masateru Ohta
- HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama230-0045, Japan
| | - Teruki Honma
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
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15
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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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16
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Chadi MA, Mousannif H, Aamouche A. Conditional reduction of the loss value versus reinforcement learning for biassing a de-novo drug design generator. J Cheminform 2022; 14:65. [PMID: 36167559 PMCID: PMC9516832 DOI: 10.1186/s13321-022-00643-2] [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: 07/03/2022] [Accepted: 09/07/2022] [Indexed: 11/10/2022] Open
Abstract
Deep learning has demonstrated promising results in de novo drug design. Often, the general pipeline consists of training a generative model (G) to learn the building rules of valid molecules, then using a biassing technique such as reinforcement learning (RL) to focus G on the desired chemical space. However, this sequential training of the same model for different tasks is known to be prone to a catastrophic forgetting (CF) phenomenon. This work presents a novel yet simple approach to bias G with significantly less CF than RL. The proposed method relies on backpropagating a reduced value of the cross-entropy loss used to train G according to the proportion of desired molecules that the biased-G can generate. We named our approach CRLV, short for conditional reduction of the loss value. We compared the two biased models (RL-biased-G and CRLV-biased-G) for four different objectives related to de novo drug design.CRLV-biased-G outperformed RL-biased-G in all four objectives and manifested appreciably less CF. Besides, an intersection analysis between molecules generated by the RL-biased-G and the CRLV-biased-G revealed that they can be used jointly without losing diversity given the low percentage of overlap between the two to further increase the desirability. Finally, we show that the difficulty of an objective is proportional to (i) its frequency in the dataset used to train G and (ii) the associated structural variance (SV), which is a new parameter we introduced in this paper, calling for novel exploration techniques for such difficult objectives.
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Affiliation(s)
- Mohamed-Amine Chadi
- Laboratoire Ingénierie des Systems Informatiques (LISI), Department of Computer Science, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakech, Morocco.
| | - Hajar Mousannif
- Laboratoire Ingénierie des Systems Informatiques (LISI), Department of Computer Science, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakech, Morocco
| | - Ahmed Aamouche
- Laboratoire Ingénierie des Systèmes et Applications (LISA), Ecole Nationale des Sciences Appliquées de Marrakech, Cadi Ayyad University, BP 575, Avenue Abdelkrim Khattabi, 40000, Marrakech, Morocco
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17
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Li C, Wang C, Sun M, Zeng Y, Yuan Y, Gou Q, Wang G, Guo Y, Pu X. Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime. J Chem Inf Model 2022; 62:4873-4887. [PMID: 35998331 DOI: 10.1021/acs.jcim.2c00997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent neural networks (RNNs) correlated by the transfer learning strategy, to efficiently generate new energetic molecules with a high detonation velocity in the case of very limited data available. To avoid the dependence on the external big data set, data augmentation by fragment shuffling of 303 energetic compounds is utilized to produce 500,000 molecules to pretrain RNN, through which the model can learn sufficient structure knowledge. Then the pretrained RNN is fine-tuned by focusing on the 303 energetic compounds to generate 7153 molecules similar to the energetic compounds. In order to more reliably screen the molecules with a high detonation velocity, the SMILE enumeration augmentation coupled with the pretrained knowledge is utilized to build an RNN-based prediction model, through which R2 is boosted from 0.4446 to 0.9572. The comparable performance with the transfer learning strategy based on an existing big database (ChEMBL) to produce the energetic molecules and drug-like ones further supports the effectiveness and generality of our strategy in the low data regime. High-precision quantum mechanics calculations further confirm that 35 new molecules present a higher detonation velocity and lower synthetic accessibility than the classic explosive RDX, along with good thermal stability. In particular, three new molecules are comparable to caged CL-20 in the detonation velocity. All the source codes and the data set are freely available at https://github.com/wangchenghuidream/RNNMGM.
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Affiliation(s)
- Chuan Li
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Chenghui Wang
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Ming Sun
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yan Zeng
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Yuan Yuan
- College of Management, Southwest University for Nationalities, Chengdu 610041, China
| | - Qiaolin Gou
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Guangchuan Wang
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
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18
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Amrane M, Oukid S, Ensari T. SMILES generation against Covid-19 with Encoder-Decoder LSTM and PCA's features. J MECH MED BIOL 2022. [DOI: 10.1142/s0219519422500725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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19
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Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review. Comput Biol Med 2022; 149:106020. [DOI: 10.1016/j.compbiomed.2022.106020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/16/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022]
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20
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Detecting Drug–Target Interactions with Feature Similarity Fusion and Molecular Graphs. BIOLOGY 2022; 11:biology11070967. [PMID: 36101348 PMCID: PMC9312204 DOI: 10.3390/biology11070967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/12/2022] [Accepted: 06/24/2022] [Indexed: 12/03/2022]
Abstract
Simple Summary Accurate identification of potential targets for drugs to interact with can accelerate drug development. The identification of drug–target interactions can provide insights into hidden drug efficacy. This paper presents a prediction model based on feature similarity fusion that can identify crucial features of drugs and targets to help predict drug–target interactions. Abstract The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug–target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug–target interactions.
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21
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Saldívar-González FI, Medina-Franco JL. Approaches for enhancing the analysis of chemical space for drug discovery. Expert Opin Drug Discov 2022; 17:789-798. [PMID: 35640229 DOI: 10.1080/17460441.2022.2084608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods. AREAS COVERED The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods. EXPERT OPINION Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.
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Affiliation(s)
- Fernanda I Saldívar-González
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
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22
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Martinelli DD. Generative machine learning for de novo drug discovery: A systematic review. Comput Biol Med 2022; 145:105403. [PMID: 35339849 DOI: 10.1016/j.compbiomed.2022.105403] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 02/08/2023]
Abstract
Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have revolutionized de novo drug discovery, rendering the explorative process more efficient. Several model frameworks and input formats have been proposed to enhance the performance of intelligent algorithms in generative molecular design. In this systematic literature review of experimental articles and reviews over the last five years, machine learning models, challenges associated with computational molecule design along with proposed solutions, and molecular encoding methods are discussed. A query-based search of the PubMed, ScienceDirect, Springer, Wiley Online Library, arXiv, MDPI, bioRxiv, and IEEE Xplore databases yielded 87 studies. Twelve additional studies were identified via citation searching. Of the articles in which machine learning was implemented, six prominent algorithms were identified: long short-term memory recurrent neural networks (LSTM-RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), adversarial autoencoders (AAEs), evolutionary algorithms, and gated recurrent unit (GRU-RNNs). Furthermore, eight central challenges were designated: homogeneity of generated molecular libraries, deficient synthesizability, limited assay data, model interpretability, incapacity for multi-property optimization, incomparability, restricted molecule size, and uncertainty in model evaluation. Molecules were encoded either as strings, which were occasionally augmented using randomization, as 2D graphs, or as 3D graphs. Statistical analysis and visualization are performed to illustrate how approaches to machine learning in de novo drug design have evolved over the past five years. Finally, future opportunities and reservations are discussed.
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23
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Shi W, Singha M, Srivastava G, Pu L, Ramanujam J, Brylinski M. Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design. Front Pharmacol 2022; 13:837715. [PMID: 35359869 PMCID: PMC8962739 DOI: 10.3389/fphar.2022.837715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/10/2022] [Indexed: 11/13/2022] Open
Abstract
Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic molecules are being discovered in numerous proteins linked to various diseases. These valuable data offer new opportunities to build efficient computational models predicting binding molecules for target sites through the application of data mining and machine learning. In particular, deep neural networks are powerful techniques capable of learning from complex data in order to make informed drug binding predictions. In this communication, we describe Pocket2Drug, a deep graph neural network model to predict binding molecules for a given a ligand binding site. This approach first learns the conditional probability distribution of small molecules from a large dataset of pocket structures with supervised training, followed by the sampling of drug candidates from the trained model. Comprehensive benchmarking simulations show that using Pocket2Drug significantly improves the chances of finding molecules binding to target pockets compared to traditional drug selection procedures. Specifically, known binders are generated for as many as 80.5% of targets present in the testing set consisting of dissimilar data from that used to train the deep graph neural network model. Overall, Pocket2Drug is a promising computational approach to inform the discovery of novel biopharmaceuticals.
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Affiliation(s)
- Wentao Shi
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, United States
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, United States
| | - J. Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, United States
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, United States
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, United States
- *Correspondence: Michal Brylinski,
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Deep Learning Applied to Ligand-Based De Novo Drug Design. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:273-299. [PMID: 34731474 DOI: 10.1007/978-1-0716-1787-8_12] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on artificial intelligent (AI) algorithms, with a particular focus on ligand-based methods. We start this review by reporting a brief overview of the most relevant de novo design approaches developed before the use of AI techniques. We then describe the nowadays most common neural network architectures employed in ligand-based de novo design, together with an up-to-date list of more than 100 deep generative models found in the literature (2017-2020). In order to show how deep generative approaches are applied into drug discovery context, we report all the now available studies in which generated compounds have been synthetized and their biological activity tested. Finally, we discuss what we envisage as beneficial future directions for further application of deep generative models in de novo drug design.
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25
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Wang F, Feng X, Guo X, Xu L, Xie L, Chang S. Improving de novo Molecule Generation by Embedding LSTM and Attention Mechanism in CycleGAN. Front Genet 2021; 12:709500. [PMID: 34422013 PMCID: PMC8376287 DOI: 10.3389/fgene.2021.709500] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
The application of deep learning in the field of drug discovery brings the development and expansion of molecular generative models along with new challenges in this field. One of challenges in de novo molecular generation is how to produce new reasonable molecules with desired pharmacological, physical, and chemical properties. To improve the similarity between the generated molecule and the starting molecule, we propose a new molecule generation model by embedding Long Short-Term Memory (LSTM) and Attention mechanism in CycleGAN architecture, LA-CycleGAN. The network layer of the generator in CycleGAN is fused head and tail to improve the similarity of the generated structure. The embedded LSTM and Attention mechanism can overcome long-term dependency problems in treating the normally used SMILES input. From our quantitative evaluation, we present that LA-CycleGAN expands the chemical space of the molecules and improves the ability of structure conversion. The generated molecules are highly similar to the starting compound structures while obtaining expected molecular properties during cycle generative adversarial network learning, which comprehensively improves the performance of the generative model.
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Affiliation(s)
- Feng Wang
- Changzhou University Huaide College, Taizhou, China.,School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, China
| | | | - Xiao Guo
- Changzhou University Huaide College, Taizhou, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, China
| | - Liangxu Xie
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, China
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26
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Peng SP, Yang XY, Zhao Y. Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning. Int J Mol Sci 2021; 22:9099. [PMID: 34445805 PMCID: PMC8396663 DOI: 10.3390/ijms22169099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/19/2021] [Accepted: 08/20/2021] [Indexed: 11/29/2022] Open
Abstract
The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted information of molecular structures and provide interpretability. By combining the generative and prediction models, thousands of NFAs with required frontier molecular orbital energies are generated. The generated new molecules essentially explore the chemical space and enrich the database of transformation rules for molecular design. The conditional generation model can also be trained to generate the molecules from molecular fragments, and the contribution of molecular fragments to the properties is subsequently predicted by the prediction model.
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Affiliation(s)
| | | | - Yi Zhao
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Fujian Provincial Key Lab of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (S.-P.P.); (X.-Y.Y.)
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27
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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28
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Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021; 26:2515-2526. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/12/2021] [Accepted: 07/01/2021] [Indexed: 12/23/2022]
Abstract
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.
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Affiliation(s)
- Michel L Leite
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Department of Molecular Biology, Biological Sciences Institute, University of Brasília, Campus Darcy Ribeiro, Block K, 70.790-900, Brasilia, Federal District, Brazil
| | - Lorena S de Loiola Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor A Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor Kreniski
- Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil
| | | | - Nicolau B da Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Fabricio F Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil; Cancer Biology and Epigenomics Program, Ann & Robert H Lurie Children's Hospital of Chicago Research Center and Northwestern University's Feinberg School of Medicine, 2430 N. Halsted St, Box 220, Chicago, IL 60614, USA; MATTER Chicago, 222 W. Merchandise Mart Plaza, Suite 12th Floor, Chicago, IL 60654, USA; Genomic Enterprise, San Diego, CA 92008, USA; Genomic Enterprise, New York, NY 11581, USA.
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Moreira-Filho JT, Silva AC, Dantas RF, Gomes BF, Souza Neto LR, Brandao-Neto J, Owens RJ, Furnham N, Neves BJ, Silva-Junior FP, Andrade CH. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence. Front Immunol 2021; 12:642383. [PMID: 34135888 PMCID: PMC8203334 DOI: 10.3389/fimmu.2021.642383] [Citation(s) in RCA: 3] [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: 12/15/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.
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Affiliation(s)
- José T. Moreira-Filho
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Arthur C. Silva
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Rafael F. Dantas
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Barbara F. Gomes
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lauro R. Souza Neto
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Jose Brandao-Neto
- Diamond Light Source Ltd., Didcot, United Kingdom
- Research Complex at Harwell, Didcot, United Kingdom
| | - Raymond J. Owens
- The Rosalind Franklin Institute, Harwell, United Kingdom
- Division of Structural Biology, The Wellcome Centre for Human Genetic, University of Oxford, Oxford, United Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bruno J. Neves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carolina H. Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
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Srinivas R, Verma N, Kraka E, Larson EC. Deep Learning-Based Ligand Design Using Shared Latent Implicit Fingerprints from Collaborative Filtering. J Chem Inf Model 2021; 61:2159-2174. [PMID: 33899481 DOI: 10.1021/acs.jcim.0c01355] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In their previous work, Srinivas et al. [ J. Cheminf. 2018, 10, 56] have shown that implicit fingerprints capture ligands and proteins in a shared latent space, typically for the purposes of virtual screening with collaborative filtering models applied on known bioactivity data. In this work, we extend these implicit fingerprints/descriptors using deep learning techniques to translate latent descriptors into discrete representations of molecules (SMILES), without explicitly optimizing for chemical properties. This allows the design of new compounds based upon the latent representation of nearby proteins, thereby encoding druglike properties including binding affinities to known proteins. The implicit descriptor method does not require any fingerprint similarity search, which makes the method free of any bias arising from the empirical nature of the fingerprint models [Srinivas, R.; J. Cheminf. 2018, 10, 56]. We evaluate the properties of the potentially novel drugs generated by our approach using physical properties of druglike molecules and chemical complexity. Additionally, we analyze the reliability of the biological activity of the new compounds generated using this method by employing models of protein-ligand interaction, which assists in assessing the potential binding affinity of the designed compounds. We find that the generated compounds exhibit properties of chemically feasible compounds and are predicted to be excellent binders to known proteins. Furthermore, we also analyze the diversity of compounds created using the Tanimoto distance and conclude that there is a wide diversity in the generated compounds.
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Affiliation(s)
- Raghuram Srinivas
- Department of Computer Science, Southern Methodist University, Dallas, Texas 75205, United States
| | - Niraj Verma
- Department of Chemistry, Southern Methodist University, Dallas, Texas 75205, United States
| | - Elfi Kraka
- Department of Chemistry, Southern Methodist University, Dallas, Texas 75205, United States
| | - Eric C Larson
- Department of Computer Science, Southern Methodist University, Dallas, Texas 75205, United States
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Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, Lynch I, Greco D, Melagraki G. Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int J Mol Sci 2021; 22:1676. [PMID: 33562347 PMCID: PMC7915729 DOI: 10.3390/ijms22041676] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/31/2021] [Accepted: 01/31/2021] [Indexed: 12/11/2022] Open
Abstract
. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.
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Affiliation(s)
| | - Antreas Afantitis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus;
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
| | - Anastasios G. Papadiamantis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus;
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Vassilis Aidinis
- Institute for Bioinnovation, Biomedical Sciences Research Center Alexander Fleming, Fleming 34, 16672 Athens, Greece;
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
- Finnish Center for Alternative Methods (FICAM), Tampere University, 33520 Tampere, Finland
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 16672 Vari, Greece
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Lambrinidis G, Tsantili-Kakoulidou A. Multi-objective optimization methods in novel drug design. Expert Opin Drug Discov 2020; 16:647-658. [PMID: 33353441 DOI: 10.1080/17460441.2021.1867095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.
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Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
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Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 2020; 477:4559-4580. [PMID: 33290527 PMCID: PMC7733676 DOI: 10.1042/bcj20200781] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 12/15/2022]
Abstract
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
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Elbadawi M, Gaisford S, Basit AW. Advanced machine-learning techniques in drug discovery. Drug Discov Today 2020; 26:769-777. [PMID: 33290820 DOI: 10.1016/j.drudis.2020.12.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/16/2020] [Accepted: 12/02/2020] [Indexed: 01/20/2023]
Abstract
The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery.
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Affiliation(s)
- Moe Elbadawi
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London, WC1N 1AX, UK
| | - Simon Gaisford
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London, WC1N 1AX, UK; FabRx Ltd, 3 Romney Road, Ashford, TN24 0RW, UK
| | - Abdul W Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London, WC1N 1AX, UK; FabRx Ltd, 3 Romney Road, Ashford, TN24 0RW, UK.
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Khemchandani Y, O'Hagan S, Samanta S, Swainston N, Roberts TJ, Bollegala D, Kell DB. DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. J Cheminform 2020; 12:53. [PMID: 33431037 PMCID: PMC7487898 DOI: 10.1186/s13321-020-00454-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/18/2020] [Indexed: 02/03/2023] Open
Abstract
We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.
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Affiliation(s)
- Yash Khemchandani
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
- Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400 076, India
| | - Stephen O'Hagan
- Dept of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN, UK
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
| | - Timothy J Roberts
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
| | - Danushka Bollegala
- Dept of Computer Science, University of Liverpool, Ashton Building, Ashton Street, Liverpool, L69 3BX, UK
| | - Douglas B Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 200, Kgs, 2800, Lyngby, Denmark.
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