1
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Abbas MKG, Rassam A, Karamshahi F, Abunora R, Abouseada M. The Role of AI in Drug Discovery. Chembiochem 2024; 25:e202300816. [PMID: 38735845 DOI: 10.1002/cbic.202300816] [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: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
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Affiliation(s)
- M K G Abbas
- Center for Advanced Materials, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Abrar Rassam
- Secondary Education, Educational Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Fatima Karamshahi
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Rehab Abunora
- Faculty of Medicine, General Medicine and Surgery, Helwan University, Cairo, Egypt
| | - Maha Abouseada
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
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2
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Atz K, Cotos L, Isert C, Håkansson M, Focht D, Hilleke M, Nippa DF, Iff M, Ledergerber J, Schiebroek CCG, Romeo V, Hiss JA, Merk D, Schneider P, Kuhn B, Grether U, Schneider G. Prospective de novo drug design with deep interactome learning. Nat Commun 2024; 15:3408. [PMID: 38649351 PMCID: PMC11035696 DOI: 10.1038/s41467-024-47613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Maria Håkansson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Dorota Focht
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Mattis Hilleke
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Michael Iff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Jann Ledergerber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Carl C G Schiebroek
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Valentina Romeo
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Jan A Hiss
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Petra Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Bernd Kuhn
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
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3
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Jin L, Cheng S, Ding W, Huang J, van Eldik R, Ji L. Insight into chemically reactive metabolites of aliphatic amine pollutants: A de novo prediction strategy and case study of sertraline. ENVIRONMENT INTERNATIONAL 2024; 186:108636. [PMID: 38593692 DOI: 10.1016/j.envint.2024.108636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 04/11/2024]
Abstract
The uncommon metabolic pathways of organic pollutants are easily overlooked, potentially leading to idiosyncratic toxicity. Prediction of their biotransformation associated with the toxic effects is the very purpose that this work focuses, to develop a de novo method to mechanistically predict the reactive toxicity pathways of uncommon metabolites from start aliphatic amine molecules, which employed sertraline triggered by CYP450 enzymes as a model system, as there are growing concerns about the effects on human health posed by antidepressants in the aquatic environment. This de novo prediction strategy combines computational and experimental methods, involving DFT calculations upon sequential growth, in vitro and in vivo assays, dissecting chemically reactive mechanism relevant to toxicity, and rationalizing the fundamental factors. Significantly, desaturation and debenzylation-aromatization as the emerging metabolic pathways of sertraline have been elucidated, with the detection of DNA adducts of oxaziridine metabolite in mice, highlighting the potential reactive toxicity. Molecular orbital analysis supports the reactivity preference for toxicological-relevant C-N desaturation over N-hydroxylation of sertraline, possibly extended to several other aliphatic amines based on the Bell-Evans-Polanyi principle. It was further validated toward some other wide-concerned aliphatic amine pollutants involving atrazine, ε-caprolactam, 6PPD via in silico and in vitro assays, thereby constituting a complete path for de novo prediction from case study to general applications.
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Affiliation(s)
- Lingmin Jin
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
| | - Shiyang Cheng
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
| | - Wen Ding
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
| | - Jingru Huang
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
| | - Rudi van Eldik
- Department of Chemistry and Pharmacy, University of Erlangen-Nuremberg, Egerlandstr. 1, 91058 Erlangen, Germany; Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
| | - Li Ji
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
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Fukunaga I, Matsukiyo Y, Kaitoh K, Yamanishi Y. Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization. Mol Inform 2024; 43:e202300148. [PMID: 38182544 DOI: 10.1002/minf.202300148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 02/03/2024] [Accepted: 02/03/2024] [Indexed: 01/07/2024]
Abstract
Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.
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Affiliation(s)
- Itsuki Fukunaga
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yuki Matsukiyo
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
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5
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Afzal M, Qais FA, Abduh NA, Christy M, Ayub R, Alarifi A. Identification of bioactive compounds of Zanthoxylum armatum as potential inhibitor of pyruvate kinase M2 (PKM2): Computational and virtual screening approaches. Heliyon 2024; 10:e27361. [PMID: 38495183 PMCID: PMC10943388 DOI: 10.1016/j.heliyon.2024.e27361] [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: 09/30/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024] Open
Abstract
PKM2 (Pyruvate kinase M2) is the isoform of pyruvate kinase which is known to catalyse the last step of glycolysis that is responsible for energy production. This specific isoform is known to be highly expressed in certain cancerous conditions. Considering the role of this protein in various cancer conditions, we used PKM2 as a target protein to identify the potential compounds against this target. In this study, we have examined 96 compounds of Zanthoxylum armatum using an array of computational and in silico tools. The compounds were assessed for toxicity then their anticancer potential was predicted. The virtual screening was done with molecular docking followed by a detailed examination using molecular dynamics simulation. The majority of the compounds showed a higher probability of being antineoplastic. Based on toxicity, predicted anticancer potential, binding affinity, and binding site, three compounds (nevadensin, asarinin, and kaempferol) were selected as hit compounds. The binding energy of these compounds with PKM2 ranged from -7.7 to -8.3 kcal/mol and all hit compounds interact at the active site of the protein. The selected hit compounds formed a stable complex with PKM2 when simulated under physiological conditions. The dynamic analysis showed that these compounds remained attached to the active site till the completion of molecular simulation. MM-PBSA analysis showed that nevadensin exhibited a higher affinity towards PKM2 compared to asarinin and kaempferol. These compounds need to be assessed properties in vivo and in vitro to validate their efficacy.
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Affiliation(s)
- Mohd Afzal
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Faizan Abul Qais
- Department of Agricultural Microbiology, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh, UP, 202002, India
| | - Naaser A.Y. Abduh
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Maria Christy
- Department of Energy Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Rashid Ayub
- Department of Science Technology and Innovation, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Abdullah Alarifi
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
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6
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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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7
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Yi JC, Yang ZY, Zhao WT, Yang ZJ, Zhang XC, Wu CK, Lu AP, Cao DS. ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization. Brief Bioinform 2024; 25:bbae008. [PMID: 38385872 PMCID: PMC10883642 DOI: 10.1093/bib/bbae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/17/2023] [Accepted: 01/02/2024] [Indexed: 02/23/2024] Open
Abstract
Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.
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Affiliation(s)
- Jia-Cai Yi
- School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Wen-Tao Zhao
- School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Xiao-Chen Zhang
- School of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, PR China
| | - Cheng-Kun Wu
- State Key Laboratory of High-Performance Computing, Changsha 410073, Hunan, PR China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
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8
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Olmedo DA, Durant-Archibold AA, López-Pérez JL, Medina-Franco JL. Design and Diversity Analysis of Chemical Libraries in Drug Discovery. Comb Chem High Throughput Screen 2024; 27:502-515. [PMID: 37409545 DOI: 10.2174/1386207326666230705150110] [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: 04/05/2023] [Revised: 05/30/2023] [Accepted: 05/30/2023] [Indexed: 07/07/2023]
Abstract
Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.
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Affiliation(s)
- Dionisio A Olmedo
- Centro de Investigaciones Farmacognósticas de la Flora Panameña (CIFLORPAN), Facultad de Farmacia, Universidad de Panamá, Ciudad de Panamá, Apartado, 0824-00178, Panamá
- Sistema Nacional de Investigación (SNI), Secretaria Nacional de Ciencia, Tecnología e Innovación (SENACYT), Ciudad del Saber, Clayton, Panamá
| | - Armando A Durant-Archibold
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Apartado, 0843-01103, Panamá
- Departamento de Bioquímica, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - José Luis López-Pérez
- CESIFAR, Departamento de Farmacología, Facultad de Medicina, Universidad de Panamá, Ciudad de Panamá, Panamá
- Departamento de Ciencias Farmacéuticas, Facultad de Farmacia, Universidad de Salamanca, Avda. Campo Charro s/n, 37071 Salamanca, España
| | - José Luis Medina-Franco
- DIFACQUIM Grupo de Investigación, Departamento de Farmacia, Escuela de Química, Universidad Nacional Autónoma de México, Ciudad de México, Apartado, 04510, México
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9
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Gu R, Wu F, Huang Z. Role of Computer-Aided Drug Design in Drug Development. Molecules 2023; 28:7160. [PMID: 37894639 PMCID: PMC10609497 DOI: 10.3390/molecules28207160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The introduction of computational techniques to pharmaceutical chemistry and molecular biology in the 20th century has changed the way people develop drugs [...].
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Affiliation(s)
- Ruoxu Gu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fengxu Wu
- Hubei Key Laboratory of Wudang Local Chinese Medicine Research, School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan 442000, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
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10
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Sauer S, Matter H, Hessler G, Grebner C. Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning. J Chem Inf Model 2023; 63:5709-5726. [PMID: 37668352 DOI: 10.1021/acs.jcim.3c00735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Lead optimization supported by artificial intelligence (AI)-based generative models has become increasingly important in drug design. Success factors are reagent availability, novelty, and the optimization of multiple properties. Directed fragment-replacement is particularly attractive, as it mimics medicinal chemistry tactics. Here, we present variations of fragment-based reinforcement learning using an actor-critic model. Novel features include freezing fragments and using reagents as the fragment source. Splitting molecules according to reaction schemes improves synthesizability, while tuning network output probabilities allows us to balance novelty versus diversity. Combining fragment-based optimization with virtual library encodings allows the exploration of large chemical spaces with synthesizable ideas. Collectively, these enhancements influence design toward high-quality molecules with favorable profiles. A validation study using 15 pharmaceutically relevant targets reveals that novel structures are obtained for most cases, which are identical or related to independent validation sets for each target. Hence, these modifications significantly increase the value of fragment-based reinforcement learning for drug design. The code is available on GitHub: https://github.com/Sanofi-Public/IDD-papers-fragrl.
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Affiliation(s)
- Susanne Sauer
- Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
| | - Hans Matter
- Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
| | - Gerhard Hessler
- Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
| | - Christoph Grebner
- Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, 65926 Frankfurt am Main, Germany
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11
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Lamanna G, Delre P, Marcou G, Saviano M, Varnek A, Horvath D, Mangiatordi GF. GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design. J Chem Inf Model 2023; 63:5107-5119. [PMID: 37556857 PMCID: PMC10466378 DOI: 10.1021/acs.jcim.3c00963] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Indexed: 08/11/2023]
Abstract
This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.
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Affiliation(s)
- Giuseppe Lamanna
- Chemistry
Department, University of Bari “Aldo
Moro”, Via E.
Orabona, 4, I-70125 Bari, Italy
- CNR
− Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Pietro Delre
- CNR
− Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Gilles Marcou
- Laboratoire
de Chémoinformatique UMR7140, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Michele Saviano
- CNR
− Institute of Crystallography, Via Vivaldi 43, 81100 Caserta, Italy
| | - Alexandre Varnek
- Laboratoire
de Chémoinformatique UMR7140, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Dragos Horvath
- Laboratoire
de Chémoinformatique UMR7140, 4 rue Blaise Pascal, 67000 Strasbourg, France
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12
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McDonald SM, Augustine EK, Lanners Q, Rudin C, Catherine Brinson L, Becker ML. Applied machine learning as a driver for polymeric biomaterials design. Nat Commun 2023; 14:4838. [PMID: 37563117 PMCID: PMC10415291 DOI: 10.1038/s41467-023-40459-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/24/2023] [Indexed: 08/12/2023] Open
Abstract
Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.
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Affiliation(s)
| | - Emily K Augustine
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Quinn Lanners
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Cynthia Rudin
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - L Catherine Brinson
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Matthew L Becker
- Department of Chemistry, Duke University, Durham, NC, USA.
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA.
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13
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Ciepliński T, Danel T, Podlewska S, Jastrzȩbski S. Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark. J Chem Inf Model 2023. [PMID: 37224003 DOI: 10.1021/acs.jcim.2c01355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a widely used computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that various graph-based generative models fail to propose molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we also include simpler tasks in the benchmark based on a simpler scoring function. We release the benchmark as an easy to use package available at https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone toward the goal of automatically generating promising drug candidates.
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Affiliation(s)
- Tobiasz Ciepliński
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
| | - Tomasz Danel
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Smȩtna 12, 31-343 Kraków, Poland
| | - Stanisław Jastrzȩbski
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
- Molecule.one, Al. Jerozolimskie 96, 00-807 Warsaw, Poland
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14
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Dost K, Pullar-Strecker Z, Brydon L, Zhang K, Hafner J, Riddle PJ, Wicker JS. Combatting over-specialization bias in growing chemical databases. J Cheminform 2023; 15:53. [PMID: 37208694 DOI: 10.1186/s13321-023-00716-w] [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: 10/04/2022] [Accepted: 03/25/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Predicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive models can be data-driven using Machine Learning or based on researchers' experience and depend on the collection of past results. In either case: models (or researchers) can only make reliable assumptions about compounds that are similar to what they have seen before. Therefore, consequent usage of these predictive models shapes the dataset and causes a continuous specialization shrinking the applicability domain of all trained models on this dataset in the future, and increasingly harming model-based exploration of the space. PROPOSED SOLUTION In this paper, we propose CANCELS (CounterActiNg Compound spEciaLization biaS), a technique that helps to break the dataset specialization spiral. Aiming for a smooth distribution of the compounds in the dataset, we identify areas in the space that fall short and suggest additional experiments that help bridge the gap. Thereby, we generally improve the dataset quality in an entirely unsupervised manner and create awareness of potential flaws in the data. CANCELS does not aim to cover the entire compound space and hence retains a desirable degree of specialization to a specified research domain. RESULTS An extensive set of experiments on the use-case of biodegradation pathway prediction not only reveals that the bias spiral can indeed be observed but also that CANCELS produces meaningful results. Additionally, we demonstrate that mitigating the observed bias is crucial as it cannot only intervene with the continuous specialization process, but also significantly improves a predictor's performance while reducing the number of required experiments. Overall, we believe that CANCELS can support researchers in their experimentation process to not only better understand their data and potential flaws, but also to grow the dataset in a sustainable way. All code is available under github.com/KatDost/Cancels .
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Affiliation(s)
- Katharina Dost
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand.
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany.
| | - Zac Pullar-Strecker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Liam Brydon
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Kunyang Zhang
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Jasmin Hafner
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Patricia J Riddle
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Jörg S Wicker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany
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15
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Koutroumpa NM, Papavasileiou KD, Papadiamantis AG, Melagraki G, Afantitis A. A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation. Int J Mol Sci 2023; 24:6573. [PMID: 37047543 PMCID: PMC10095548 DOI: 10.3390/ijms24076573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.
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Affiliation(s)
- Nikoletta-Maria Koutroumpa
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
| | - Konstantinos D. Papavasileiou
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
- Department of ChemoInformatics, NovaMechanics MIKE., 185 45 Piraeus, Greece
| | - Anastasios G. Papadiamantis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 166 73 Vari, Greece
| | - Antreas Afantitis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
- Department of ChemoInformatics, NovaMechanics MIKE., 185 45 Piraeus, Greece
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16
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Grasso D, Galderisi S, Santucci A, Bernini A. Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology. Int J Mol Sci 2023; 24:ijms24065819. [PMID: 36982893 PMCID: PMC10054308 DOI: 10.3390/ijms24065819] [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: 02/05/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.
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Affiliation(s)
- Daniela Grasso
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Silvia Galderisi
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Andrea Bernini
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
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17
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Tysinger EP, Rai BK, Sinitskiy AV. Can We Quickly Learn to "Translate" Bioactive Molecules with Transformer Models? J Chem Inf Model 2023; 63:1734-1744. [PMID: 36914216 DOI: 10.1021/acs.jcim.2c01618] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a type of machine learning (ML) model originally developed for machine translation. By training transformer models on pairs of similar bioactive molecules from the public ChEMBL data set, we enable them to learn medicinal-chemistry-meaningful, context-dependent transformations of molecules, including those absent from the training set. By retrospective analysis on the performance of transformer models on ChEMBL subsets of ligands binding to COX2, DRD2, or HERG protein targets, we demonstrate that the models can generate structures identical or highly similar to most active ligands, despite the models having not seen any ligands active against the corresponding protein target during training. Our work demonstrates that human experts working on hit expansion in drug design can easily and quickly employ transformer models, originally developed to translate texts from one natural language to another, to "translate" from known molecules active against a given protein target to novel molecules active against the same target.
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Affiliation(s)
- Emma P Tysinger
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Brajesh K Rai
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Anton V Sinitskiy
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
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18
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An S, Hwang SY, Gong J, Ahn S, Park IG, Oh S, Chin YW, Noh M. Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis. J Chem Inf Model 2023; 63:856-869. [PMID: 36716271 DOI: 10.1021/acs.jcim.3c00033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In silico machine learning applications for phenotype-based screening have primarily been limited due to the lack of machine-readable data related to disease phenotypes. Adiponectin, a nuclear receptor (NR)-regulated adipocytokine, is relatively downregulated in human metabolic diseases. Here, we present a machine-learning model to predict the adiponectin-secretion-promoting activity of flavonoid-associated phytochemicals (FAPs). We modeled a structure-activity relationship between the chemical similarity of FAPs and their bioactivities using a random forest-based classifier, which provided the NR activity of each FAP as a probability. To link the classifier-predicted NR activity to the phenotype, we next designed a single-cell transcriptomics-based multiple linear regression model to generate the relative adiponectin score (RAS) of FAPs. In experimental validation, estimated RAS values of FAPs isolated from Scutellaria baicalensis exhibited a significant correlation with their adiponectin-secretion-promoting activity. The combined cheminformatics and bioinformatics approach enables the computational reconstruction of phenotype-based screening systems.
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Affiliation(s)
- Seungchan An
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Seok Young Hwang
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Junpyo Gong
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Sungjin Ahn
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - In Guk Park
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Soyeon Oh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Young-Won Chin
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Minsoo Noh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
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19
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Atz K, Guba W, Grether U, Schneider G. Machine Learning and Computational Chemistry for the Endocannabinoid System. Methods Mol Biol 2023; 2576:477-493. [PMID: 36152211 DOI: 10.1007/978-1-0716-2728-0_39] [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: 06/16/2023]
Abstract
Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | - Wolfgang Guba
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
- ETH Singapore SEC Ltd, Singapore, Singapore
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20
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Kumar R, Sharma A, Alexiou A, Ashraf GM. Artificial Intelligence in De novo Drug Design: Are We Still There? Curr Top Med Chem 2022; 22:2483-2492. [PMID: 36263480 DOI: 10.2174/1568026623666221017143244] [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: 05/03/2022] [Revised: 09/06/2022] [Accepted: 09/15/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND The artificial intelligence (AI)-assisted design of drug candidates with novel structures and desired properties has received significant attention in the recent past, so related areas of forward prediction that aim to discover chemical matters worth synthesizing and further experimental investigation. OBJECTIVES The purpose behind developing AI-driven models is to explore the broader chemical space and suggest new drug candidate scaffolds with promising therapeutic value. Moreover, it is anticipated that such AI-based models may not only significantly reduce the cost and time but also decrease the attrition rate of drug candidates that fail to reach the desirable endpoints at the final stages of drug development. In an attempt to develop AI-based models for de novo drug design, numerous methods have been proposed by various study groups by applying machine learning and deep learning algorithms to chemical datasets. However, there are many challenges in obtaining accurate predictions, and real breakthroughs in de novo drug design are still scarce. METHODS In this review, we explore the recent trends in developing AI-based models for de novo drug design to assess the current status, challenges, and opportunities in the field. CONCLUSION The consistently improved AI algorithms and the abundance of curated training chemical data indicate that AI-based de novo drug design should perform better than the current models. Improvements in the performance are warranted to obtain better outcomes in the form of potential drug candidates, which can perform well in in vivo conditions, especially in the case of more complex diseases.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh, India
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
| | - Athanasios Alexiou
- Novel Global Community Educational Foundation, Hebersham, 2770 NSW, Australia.,AFNP Med Austria, 1010 Wien, Austria
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit (PCRU), King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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21
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Bieniek MK, Cree B, Pirie R, Horton JT, Tatum NJ, Cole DJ. An open-source molecular builder and free energy preparation workflow. Commun Chem 2022; 5:136. [PMID: 36320862 PMCID: PMC9607723 DOI: 10.1038/s42004-022-00754-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 10/11/2022] [Indexed: 01/27/2023] Open
Abstract
Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.
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Affiliation(s)
- Mateusz K. Bieniek
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Ben Cree
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Rachael Pirie
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Joshua T. Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Natalie J. Tatum
- Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
| | - Daniel J. Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
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22
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Ruchawapol C, Fu WW, Xu HX. A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2022; 104:154324. [PMID: 35841663 PMCID: PMC9259013 DOI: 10.1016/j.phymed.2022.154324] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/23/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed. PURPOSE This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases. METHODS A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles. RESULTS In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks. CONCLUSION Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.
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Affiliation(s)
- Chattarin Ruchawapol
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China; Engineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China
| | - Wen-Wei Fu
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China; Engineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China.
| | - Hong-Xi Xu
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China; Engineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China.
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23
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Liu Z, Du J, Lin Z, Li Z, Liu B, Cui Z, Fang J, Xie L. DenovoProfiling: A webserver for de novo generated molecule library profiling. Comput Struct Biotechnol J 2022; 20:4082-4097. [PMID: 36016718 PMCID: PMC9379519 DOI: 10.1016/j.csbj.2022.07.045] [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: 03/03/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 01/10/2023] Open
Abstract
Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.
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Key Words
- DDR1, Discovered potent discoidin domain receptor 1
- De novo drug design
- De novo molecule library
- Deep learning
- FBDD, Fragment-based drug design
- FDR, False discovery rate
- GAN, Generative adversarial networks
- HTS, High throughput screening
- LSTM, Long short-term memory
- Library profiling
- PCA, Principal components analysis
- RNN, Recurrent neural networks
- SCA, Scaffold-based classification approach
- VAE, Variational autoencoders
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Affiliation(s)
- Zhihong Liu
- School of Public Health, Xinxiang Medical University, Xinxiang, China
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Jiewen Du
- Beijing Jingpai Technology Co., Ltd., 1500-1, Hailong Building Z-Park, Beijing 100090, China
| | - Ziying Lin
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Ze Li
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Bingdong Liu
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Zongbin Cui
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- Corresponding authors at: School of Public Health, Xinxiang Medical University, Xinxiang, China (L. Xie). Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China (J. Fang).
| | - Liwei Xie
- School of Public Health, Xinxiang Medical University, Xinxiang, China
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
- Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Corresponding authors at: School of Public Health, Xinxiang Medical University, Xinxiang, China (L. Xie). Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China (J. Fang).
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24
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Kulczyk S, Koszytkowska-Stawińska M. Novel drug design framework as a response to neglected and emerging diseases. J Biomol Struct Dyn 2022:1-12. [DOI: 10.1080/07391102.2022.2110519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Stanisław Kulczyk
- Faculty of Chemistry, Warsaw University of Technology, Warsaw, Poland
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25
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Meli R, Morris GM, Biggin PC. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review. FRONTIERS IN BIOINFORMATICS 2022; 2:885983. [PMID: 36187180 PMCID: PMC7613667 DOI: 10.3389/fbinf.2022.885983] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 01/01/2023] Open
Abstract
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.
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Affiliation(s)
- Rocco Meli
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Garrett M. Morris
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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26
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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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27
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Xie W, Wang F, Li Y, Lai L, Pei J. Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models. J Chem Inf Model 2022; 62:2269-2279. [PMID: 35544331 DOI: 10.1021/acs.jcim.2c00042] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A persistent goal for de novo drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for de novo drug design.
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Affiliation(s)
- Weixin Xie
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Fanhao Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yibo Li
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Peking-Tsinghua Center for Life Science at BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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28
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Lu C, Liu S, Shi W, Yu J, Zhou Z, Zhang X, Lu X, Cai F, Xia N, Wang Y. Systemic evolutionary chemical space exploration for drug discovery. J Cheminform 2022; 14:19. [PMID: 35365231 PMCID: PMC8973791 DOI: 10.1186/s13321-022-00598-4] [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: 12/13/2021] [Accepted: 03/11/2022] [Indexed: 11/29/2022] Open
Abstract
Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.
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Affiliation(s)
- Chong Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Shien Liu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Weihua Shi
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Jun Yu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Zhou Zhou
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Xiaoli Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Faji Cai
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Yikai Wang
- Keen Therapeutics Co., Ltd., Shanghai, China.
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29
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Harren T, Matter H, Hessler G, Rarey M, Grebner C. Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence. J Chem Inf Model 2022; 62:447-462. [PMID: 35080887 DOI: 10.1021/acs.jcim.1c01263] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important for activity. However, this information is crucial for capturing the underlying structure-activity relationships (SARs) to guide further optimization. To address this interpretation gap, "Explainable Artificial Intelligence" (XAI) methods recently became popular. Herein, we apply and compare multiple XAI methods to projects of lead optimization data sets with well-established SARs and available X-ray crystal structures. As we can show, easily understandable and comprehensive interpretations are obtained by combining DNN models with some powerful interpretation methods. In particular, SHAP-based methods are promising for this task. A novel visualization scheme using atom-based heatmaps provides useful insights into the underlying SAR. It is important to note that all interpretations are only meaningful in the context of the underlying models and associated data.
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Affiliation(s)
- Tobias Harren
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Hans Matter
- Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Gerhard Hessler
- Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christoph Grebner
- Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
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30
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Kaushik AC, Sahi S, Wei DQ. Computational Methods for Structure-Based Drug Design Through System Biology. Methods Mol Biol 2022; 2385:161-174. [PMID: 34888721 DOI: 10.1007/978-1-0716-1767-0_9] [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: 06/13/2023]
Abstract
The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods. The main advantage of this method is that it hits the fragment that has the strongest binding, has relatively small size, and leads to better compounds in terms of pharmacokinetic properties when compared with virtual screening (VS) and high-throughput screening (HTS) hits. De novo design is an essential aspect of target-based systems and requires the synthesis of chemical to allow the design of promising compound.
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Affiliation(s)
| | - Shakti Sahi
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
- Peng Cheng Laboratory, Shenzhen, Guangdong, People's Republic of China.
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31
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Jung K, Corrigan N, Wong EHH, Boyer C. Bioactive Synthetic Polymers. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2105063. [PMID: 34611948 DOI: 10.1002/adma.202105063] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/13/2021] [Indexed: 05/21/2023]
Abstract
Synthetic polymers are omnipresent in society as textiles and packaging materials, in construction and medicine, among many other important applications. Alternatively, natural polymers play a crucial role in sustaining life and allowing organisms to adapt to their environments by performing key biological functions such as molecular recognition and transmission of genetic information. In general, the synthetic and natural polymer worlds are completely separated due to the inability for synthetic polymers to perform specific biological functions; in some cases, synthetic polymers cause uncontrolled and unwanted biological responses. However, owing to the advancement of synthetic polymerization techniques in recent years, new synthetic polymers have emerged that provide specific biological functions such as targeted molecular recognition of peptides, or present antiviral, anticancer, and antimicrobial activities. In this review, the emergence of this generation of bioactive synthetic polymers and their bioapplications are summarized. Finally, the future opportunities in this area are discussed.
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Affiliation(s)
- Kenward Jung
- Cluster for Advanced Macromolecular Design (CAMD), Australian Centre for Nanomedicine (ACN), and School of Chemical Engineering, University of New South Wales (UNSW) Sydney, Sydney, NSW, 2052, Australia
| | - Nathaniel Corrigan
- Cluster for Advanced Macromolecular Design (CAMD), Australian Centre for Nanomedicine (ACN), and School of Chemical Engineering, University of New South Wales (UNSW) Sydney, Sydney, NSW, 2052, Australia
| | - Edgar H H Wong
- Cluster for Advanced Macromolecular Design (CAMD), Australian Centre for Nanomedicine (ACN), and School of Chemical Engineering, University of New South Wales (UNSW) Sydney, Sydney, NSW, 2052, Australia
| | - Cyrille Boyer
- Cluster for Advanced Macromolecular Design (CAMD), Australian Centre for Nanomedicine (ACN), and School of Chemical Engineering, University of New South Wales (UNSW) Sydney, Sydney, NSW, 2052, Australia
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32
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Transformational machine learning: Learning how to learn from many related scientific problems. Proc Natl Acad Sci U S A 2021; 118:2108013118. [PMID: 34845013 PMCID: PMC8670494 DOI: 10.1073/pnas.2108013118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2021] [Indexed: 11/18/2022] Open
Abstract
Machine learning (ML) is the branch of artificial intelligence (AI) that develops computational systems that learn from experience. In supervised ML, the ML system generalizes from labelled examples to learn a model that can predict the labels of unseen examples. Examples are generally represented using features that directly describe the examples. For instance, in drug design, ML uses features that describe molecular shape and so on. In cases where there are multiple related ML problems, it is possible to use a different type of feature: predictions made about the examples by ML models learned on other problems. We call this transformational ML. We show that this results in better predictions and improved understanding when applied to scientific problems. Almost all machine learning (ML) is based on representing examples using intrinsic features. When there are multiple related ML problems (tasks), it is possible to transform these features into extrinsic features by first training ML models on other tasks and letting them each make predictions for each example of the new task, yielding a novel representation. We call this transformational ML (TML). TML is very closely related to, and synergistic with, transfer learning, multitask learning, and stacking. TML is applicable to improving any nonlinear ML method. We tested TML using the most important classes of nonlinear ML: random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and neural networks. To ensure the generality and robustness of the evaluation, we utilized thousands of ML problems from three scientific domains: drug design, predicting gene expression, and ML algorithm selection. We found that TML significantly improved the predictive performance of all the ML methods in all the domains (4 to 50% average improvements) and that TML features generally outperformed intrinsic features. Use of TML also enhances scientific understanding through explainable ML. In drug design, we found that TML provided insight into drug target specificity, the relationships between drugs, and the relationships between target proteins. TML leads to an ecosystem-based approach to ML, where new tasks, examples, predictions, and so on synergistically interact to improve performance. To contribute to this ecosystem, all our data, code, and our ∼50,000 ML models have been fully annotated with metadata, linked, and openly published using Findability, Accessibility, Interoperability, and Reusability principles (∼100 Gbytes).
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33
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Grebner C, Matter H, Hessler G. Artificial Intelligence in Compound Design. Methods Mol Biol 2021; 2390:349-382. [PMID: 34731477 DOI: 10.1007/978-1-0716-1787-8_15] [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: 02/10/2023]
Abstract
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.
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Affiliation(s)
- Christoph Grebner
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Hans Matter
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Gerhard Hessler
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany.
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34
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Chávez-Hernández AL, Juárez-Mercado KE, Saldívar-González FI, Medina-Franco JL. Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products. Biomolecules 2021; 11:1805. [PMID: 34944448 PMCID: PMC8698858 DOI: 10.3390/biom11121805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/22/2021] [Accepted: 11/29/2021] [Indexed: 01/14/2023] Open
Abstract
Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material.
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Affiliation(s)
| | | | | | - 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; (A.L.C.-H.); (K.E.J.-M.); (F.I.S.-G.)
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35
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Xu Z, Wauchope OR, Frank AT. Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking. J Chem Inf Model 2021; 61:5589-5600. [PMID: 34633194 DOI: 10.1021/acs.jcim.1c00746] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.
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Affiliation(s)
- Ziqiao Xu
- Chemistry Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Orrette R Wauchope
- Department of Natural Sciences, City University of New York, Baruch College, New York, New York 10010, United States
| | - Aaron T Frank
- Biophysics Program, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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36
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Li Y, Pei J, Lai L. Structure-based de novo drug design using 3D deep generative models. Chem Sci 2021; 12:13664-13675. [PMID: 34760151 PMCID: PMC8549794 DOI: 10.1039/d1sc04444c] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 09/09/2021] [Indexed: 12/14/2022] Open
Abstract
Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization. DeepLigBuilder, a novel deep generative model for structure-based de novo drug design, directly generates 3D structures of drug-like compounds in the target binding site.![]()
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Affiliation(s)
- Yibo Li
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
| | - Luhua Lai
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China .,Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China .,BNLMS, College of Chemistry and Molecular Engineering, Peking University Beijing 100871 China
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37
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da Silva TH, Hachigian TZ, Lee J, King MD. Using computers to ESKAPE the antibiotic resistance crisis. Drug Discov Today 2021; 27:456-470. [PMID: 34688913 DOI: 10.1016/j.drudis.2021.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 08/01/2021] [Accepted: 10/15/2021] [Indexed: 12/16/2022]
Abstract
Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement.
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Affiliation(s)
- Thiago H da Silva
- Micron School of Materials Science and Engineering, Boise State University, Boise, ID 83725, USA
| | - Timothy Z Hachigian
- Micron School of Materials Science and Engineering, Boise State University, Boise, ID 83725, USA
| | - Jeunghoon Lee
- Micron School of Materials Science and Engineering, Boise State University, Boise, ID 83725, USA; Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA
| | - Matthew D King
- Micron School of Materials Science and Engineering, Boise State University, Boise, ID 83725, USA; Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA.
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38
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Kaitoh K, Yamanishi Y. TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning. J Chem Inf Model 2021; 61:4303-4320. [PMID: 34528432 DOI: 10.1021/acs.jcim.1c00967] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.
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Affiliation(s)
- Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
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39
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Sicho M, Liu X, Svozil D, van Westen GJP. GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics. J Cheminform 2021; 13:73. [PMID: 34563271 PMCID: PMC8465716 DOI: 10.1186/s13321-021-00550-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 09/05/2021] [Indexed: 03/05/2023] Open
Abstract
Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.
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Affiliation(s)
- M. Sicho
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
| | - X. Liu
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden, The Netherlands
| | - D. Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20 Prague 4, Czech Republic
| | - G. J. P. van Westen
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden, The Netherlands
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Muratov EN, Amaro R, Andrade CH, Brown N, Ekins S, Fourches D, Isayev O, Kozakov D, Medina-Franco JL, Merz KM, Oprea TI, Poroikov V, Schneider G, Todd MH, Varnek A, Winkler DA, Zakharov AV, Cherkasov A, Tropsha A. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem Soc Rev 2021; 50:9121-9151. [PMID: 34212944 PMCID: PMC8371861 DOI: 10.1039/d0cs01065k] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Indexed: 01/18/2023]
Abstract
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.
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Affiliation(s)
- Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
| | - Rommie Amaro
- University of California in San DiegoSan DiegoCAUSA
| | | | | | - Sean Ekins
- Collaborations PharmaceuticalsRaleighNCUSA
| | - Denis Fourches
- Department of Chemistry, North Carolina State UniversityRaleighNCUSA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Melon UniversityPittsburghPAUSA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookNYUSA
| | | | - Kenneth M. Merz
- Department of Chemistry, Michigan State UniversityEast LansingMIUSA
| | - Tudor I. Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, University of New Mexico, AlbuquerqueNMUSA
- Department of Rheumatology and Inflammation Research, Gothenburg UniversitySweden
- Novo Nordisk Foundation Center for Protein Research, University of CopenhagenDenmark
| | | | - Gisbert Schneider
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of TechnologyZurichSwitzerland
| | | | - Alexandre Varnek
- Department of Chemistry, University of StrasbourgStrasbourgFrance
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido UniversitySapporoJapan
| | - David A. Winkler
- Monash Institute of Pharmaceutical Sciences, Monash UniversityMelbourneVICAustralia
- School of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe UniversityBundooraAustralia
- School of Pharmacy, University of NottinghamNottinghamUK
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, University of British ColumbiaVancouverBCCanada
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
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Friedrich L, Cingolani G, Ko Y, Iaselli M, Miciaccia M, Perrone MG, Neukirch K, Bobinger V, Merk D, Hofstetter RK, Werz O, Koeberle A, Scilimati A, Schneider G. Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100832. [PMID: 34176236 PMCID: PMC8373093 DOI: 10.1002/advs.202100832] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/16/2021] [Indexed: 05/03/2023]
Abstract
The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.
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Affiliation(s)
- Lukas Friedrich
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
| | - Gino Cingolani
- Department of Biochemistry and Molecular BiologySidney Kimmel Cancer CenterThomas Jefferson University1020 Locust StreetPhiladelphiaPA19107USA
| | - Ying‐Hui Ko
- Department of Biochemistry and Molecular BiologySidney Kimmel Cancer CenterThomas Jefferson University1020 Locust StreetPhiladelphiaPA19107USA
| | - Mariaclara Iaselli
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Morena Miciaccia
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Maria Grazia Perrone
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Konstantin Neukirch
- Michael Popp Institute and Center for Molecular Biosciences Innsbruck (CMBI)University of InnsbruckInnsbruck6020Austria
| | - Veronika Bobinger
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
| | - Daniel Merk
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
- Institute of Pharmaceutical ChemistryGoethe‐UniversityMax‐von‐Laue Straße 9Frankfurt am Main60438Germany
| | - Robert Klaus Hofstetter
- Department of Pharmaceutical/Medicinal ChemistryFriedrich‐Schiller‐University JenaPhilosophenweg 14Jena07743Germany
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal ChemistryFriedrich‐Schiller‐University JenaPhilosophenweg 14Jena07743Germany
| | - Andreas Koeberle
- Michael Popp Institute and Center for Molecular Biosciences Innsbruck (CMBI)University of InnsbruckInnsbruck6020Austria
| | - Antonio Scilimati
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Gisbert Schneider
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
- ETH Singapore SEC Ltd1 CREATE Way, #06‐01 CREATE TowerSingapore138602Singapore
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Bolnykh V, Rossetti G, Rothlisberger U, Carloni P. Expanding the boundaries of ligand–target modeling by exascale calculations. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Viacheslav Bolnykh
- Laboratory of Computational Chemistry and Biochemistry École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM‐9)/Institute for Advanced Simulations (IAS‐5) Forschungszentrum Jülich Jülich Germany
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM‐9)/Institute for Advanced Simulations (IAS‐5) Forschungszentrum Jülich Jülich Germany
- Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany
- Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation University Hospital Aachen RWTH Aachen University Aachen Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Paolo Carloni
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (IAS‐5/INM‐9) “Computational Biomedicine” Forschungszentrum Jülich Jülich Germany
- JARA‐Institute INM‐11 “Molecular Neuroscience and Neuroimaging” Forschungszentrum Jülich Jülich Germany
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43
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Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today 2021; 26:2707-2715. [PMID: 34082136 DOI: 10.1016/j.drudis.2021.05.019] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/21/2021] [Accepted: 05/26/2021] [Indexed: 02/09/2023]
Abstract
Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years.
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Affiliation(s)
| | | | - Nathan Brown
- BenevolentAI, 4-8 Maple Street, London W1T 5HD, UK
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44
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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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45
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Zhu S, Wu M, Huang Z, An J. Trends in application of advancing computational approaches in GPCR ligand discovery. Exp Biol Med (Maywood) 2021; 246:1011-1024. [PMID: 33641446 PMCID: PMC8113737 DOI: 10.1177/1535370221993422] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.
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Affiliation(s)
- Siyu Zhu
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
- Ciechanover Institute of Precision and Regenerative Medicine, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen 518172, China
| | - Meixian Wu
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
| | - Ziwei Huang
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
- Ciechanover Institute of Precision and Regenerative Medicine, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jing An
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
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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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Al-Ostoot FH, Salah S, Khanum SA. Recent investigations into synthesis and pharmacological activities of phenoxy acetamide and its derivatives (chalcone, indole and quinoline) as possible therapeutic candidates. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2021. [PMCID: PMC7849228 DOI: 10.1007/s13738-021-02172-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Medicinal chemistry can rightfully be regarded as a cornerstone in the public health of our modern society that combines chemistry and pharmacology with the aim of designing and developing new pharmaceutical compounds. For this purpose, many chemical techniques as well as new computational chemistry applications are used to study the utilization of drugs and their biological effects. In the biological interface, medicinal chemistry constitutes a group of interdisciplinary sciences, as well as controlling its organic, physical and computational pillars. Therefore, medicinal chemists working to design an integrated and developing system that portends an era of novel and safe tailored drugs either by synthesizing new pharmaceuticals or to improving the processes by which existing pharmaceuticals are made. It includes researching the effects of synthetic, semi-synthetic and natural biologically active substances based on molecular interactions in terms of molecular structure with triggered functional groups or the specific physicochemical properties. The present work focuses on the literature survey of chemical diversity of phenoxy acetamide and its derivatives (Chalcone, Indole and Quinoline) in the molecular framework in order to get complete information regarding pharmacologically interesting compounds of widely different composition. From a biological and industrial point of view, this literature review may provide an opportunity for the chemists to design new derivatives of phenoxy acetamide and its derivatives that proved to be the successful agent in view of safety and efficacy to enhance life quality.
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Affiliation(s)
- Fares Hezam Al-Ostoot
- Department of Chemistry, Yuvaraja’s College, University of Mysore, Mysuru, 570 006 India
- Department of Biochemistry, Faculty of Education and Science, Al-Baydha University, Al-Baydha, Yemen
| | - Salma Salah
- Faculty of Medicine and Health Sciences, Thamar University, Dhamar, Yemen
| | - Shaukath Ara Khanum
- Department of Chemistry, Yuvaraja’s College, University of Mysore, Mysuru, 570 006 India
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49
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Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today 2020; 26:511-524. [PMID: 33346134 DOI: 10.1016/j.drudis.2020.12.009] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/07/2020] [Accepted: 12/11/2020] [Indexed: 12/30/2022]
Abstract
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.
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Affiliation(s)
- Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road CB2 1EW, UK; Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK.
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50
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Jeon W, Kim D. Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors. Sci Rep 2020; 10:22104. [PMID: 33328504 PMCID: PMC7744578 DOI: 10.1038/s41598-020-78537-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 11/25/2020] [Indexed: 12/16/2022] Open
Abstract
We developed a computational method named Molecule Optimization by Reinforcement Learning and Docking (MORLD) that automatically generates and optimizes lead compounds by combining reinforcement learning and docking to develop predicted novel inhibitors. This model requires only a target protein structure and directly modifies ligand structures to obtain higher predicted binding affinity for the target protein without any other training data. Using MORLD, we were able to generate potential novel inhibitors against discoidin domain receptor 1 kinase (DDR1) in less than 2 days on a moderate computer. We also demonstrated MORLD's ability to generate predicted novel agonists for the D4 dopamine receptor (D4DR) from scratch without virtual screening on an ultra large compound library. The free web server is available at http://morld.kaist.ac.kr .
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Affiliation(s)
- Woosung Jeon
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Dongsup Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
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