5
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 269] [Impact Index Per Article: 89.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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8
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Chiba S, Ohue M, Gryniukova A, Borysko P, Zozulya S, Yasuo N, Yoshino R, Ikeda K, Shin WH, Kihara D, Iwadate M, Umeyama H, Ichikawa T, Teramoto R, Hsin KY, Gupta V, Kitano H, Sakamoto M, Higuchi A, Miura N, Yura K, Mochizuki M, Ramakrishnan C, Thangakani AM, Velmurugan D, Gromiha MM, Nakane I, Uchida N, Hakariya H, Tan M, Nakamura HK, Suzuki SD, Ito T, Kawatani M, Kudoh K, Takashina S, Yamamoto KZ, Moriwaki Y, Oda K, Kobayashi D, Okuno T, Minami S, Chikenji G, Prathipati P, Nagao C, Mohsen A, Ito M, Mizuguchi K, Honma T, Ishida T, Hirokawa T, Akiyama Y, Sekijima M. A prospective compound screening contest identified broader inhibitors for Sirtuin 1. Sci Rep 2019; 9:19585. [PMID: 31863054 PMCID: PMC6925144 DOI: 10.1038/s41598-019-55069-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 11/21/2019] [Indexed: 12/17/2022] Open
Abstract
Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.
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Affiliation(s)
- Shuntaro Chiba
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,RIKEN Medical Sciences Innovation Hub Program, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan
| | | | - Petro Borysko
- Bienta/Enamine Ltd., 78 Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - Sergey Zozulya
- Bienta/Enamine Ltd., 78 Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - Nobuaki Yasuo
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Research Fellow of the Japan Society for the Promotion of Science DC1, Tokyo, Japan
| | - Ryunosuke Yoshino
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8575, Japan
| | - Kazuyoshi Ikeda
- Faculty of Pharmacy, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan
| | - Woong-Hee Shin
- Department of Biological Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, Indiana, 47907, USA.,Department of Computer Science, Purdue University, Indiana, 47907, USA
| | - Mitsuo Iwadate
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan
| | - Hideaki Umeyama
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan
| | - Takaaki Ichikawa
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan
| | - Reiji Teramoto
- Discovery technology research department, Research division, Chugai Pharmaceutical Co.,Ltd., 200, Kajiwara, Kamakura, Kanagawa, 247-8530, Japan
| | - Kun-Yi Hsin
- Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami, Okinawa, 904-0495, Japan
| | - Vipul Gupta
- The Systems Biology Research Institute, Falcon Building 5F, 5-6-9 Shirokanedai, Minato-ku, Tokyo, 108-0071, Japan
| | - Hiroaki Kitano
- Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami, Okinawa, 904-0495, Japan.,The Systems Biology Research Institute, Falcon Building 5F, 5-6-9 Shirokanedai, Minato-ku, Tokyo, 108-0071, Japan.,Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
| | - Mika Sakamoto
- Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan
| | - Akiko Higuchi
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
| | - Nobuaki Miura
- Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan
| | - Kei Yura
- Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan.,Center for Simulation Science and Informational Biology, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan.,School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan
| | - Masahiro Mochizuki
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,IMSBIO Co., Ltd., Level 6 OWL TOWER, 4-21-1 Higashi-Ikebukuro, Toshima-ku, Tokyo, 170-0013, Japan
| | - Chandrasekaran Ramakrishnan
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India
| | - A Mary Thangakani
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India
| | - D Velmurugan
- CAS in Crystallography and Biophysics and Bioinformatics Facility, University of Madras, Chennai, 600025, Tamilnadu, India
| | - M Michael Gromiha
- Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India
| | - Itsuo Nakane
- Okazaki City Hall, 2-9 Juo-cho Okazaki, Aichi, 444-8601, Japan
| | - Nanako Uchida
- IQVIA Services Japan K.K., 4-10-18 Takanawa Minato-ku, Tokyo, 108-0074, Japan
| | - Hayase Hakariya
- Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan.,Training Program of Leaders for Integrated Medical System (LIMS), Kyoto University, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Modong Tan
- Department of Chemistry & Biotechnology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
| | - Hironori K Nakamura
- Biomodeling Research Co., Ltd., 1-704-2 Uedanishi, Tenpaku-ku, Nagoya, 468-0058, Japan
| | - Shogo D Suzuki
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Tomoki Ito
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Masahiro Kawatani
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Kentaroh Kudoh
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Sakurako Takashina
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Kazuki Z Yamamoto
- Isotope Science Center, The University of Tokyo, 2-11- 16, Yayoi, Bunkyo-ku, Tokyo, 113-0032, Japan
| | - Yoshitaka Moriwaki
- Department of Biotechnology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Keita Oda
- Google Japan Inc., 6-10-1 Roppongi, Minato-ku, Tokyo, 106-6126, Japan.,Otemachi Bldg. 3F, 1-6-1, Preferred Networks, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Daisuke Kobayashi
- Department of Computational Science and Engineering, Nagoya University, Furocho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Tatsuya Okuno
- Tosei General Hospital, 160 Nishioiwake-cho, Seto, Aichi, 489-8642, Japan
| | - Shintaro Minami
- Department of Complex Systems Science, Graduate School of Information Science, Nagoya University, Furocho, Chikusa, Nagoya, 464-8601, Japan
| | - George Chikenji
- Department of Computational Science and Engineering, Nagoya University, Furocho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Philip Prathipati
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Chioko Nagao
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Attayeb Mohsen
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Mari Ito
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Kenji Mizuguchi
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Teruki Honma
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,RIKEN Center for Biosystems Dynamic Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Takashi Ishida
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan
| | - Takatsugu Hirokawa
- Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8575, Japan.,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan
| | - Yutaka Akiyama
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan
| | - Masakazu Sekijima
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan. .,Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan. .,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan.
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