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Konagaya A, Gutmann G, Zhang Y. Co-creation environment with cloud virtual reality and real-time artificial intelligence toward the design of molecular robots. J Integr Bioinform 2023; 20:jib-2022-0017. [PMID: 36194394 PMCID: PMC10063180 DOI: 10.1515/jib-2022-0017] [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: 03/15/2022] [Revised: 08/31/2022] [Accepted: 09/07/2022] [Indexed: 11/15/2022] Open
Abstract
This paper describes the design philosophy for our cloud-based virtual reality (VR) co-creation environment (CCE) for molecular modeling. Using interactive VR simulation can provide enhanced perspectives in molecular modeling for intuitive live demonstration and experimentation in the CCE. Then the use of the CCE can enhance knowledge creation by bringing people together to share and create ideas or knowledge that may not emerge otherwise. Our prototype CCE discussed here, which was developed to demonstrate our design philosophy, has already enabled multiple members to log in and touch virtual molecules running on a cloud server with no noticeable network latency via real-time artificial intelligence techniques. The CCE plays an essential role in the rational design of molecular robot parts, which consist of bio-molecules such as DNA and protein molecules.
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Affiliation(s)
- Akihiko Konagaya
- Molecular Robotics Research Institute, Co., Ltd., 4259-3, Nagatsuta, Midori, Yokohama, Japan
- Keisen University, 2-10-1, Minamino, Tama, Tokyo, Japan
| | - Gregory Gutmann
- Molecular Robotics Research Institute, Co., Ltd., 4259-3, Nagatsuta, Midori, Yokohama, Japan
| | - Yuhui Zhang
- Molecular Robotics Research Institute, Co., Ltd., 4259-3, Nagatsuta, Midori, Yokohama, Japan
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Creanza TM, Lamanna G, Delre P, Contino M, Corriero N, Saviano M, Mangiatordi GF, Ancona N. DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues. J Chem Inf Model 2022; 62:1411-1424. [PMID: 35294184 DOI: 10.1021/acs.jcim.2c00205] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.
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Affiliation(s)
- Teresa Maria Creanza
- CNR─Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - 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
- 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
| | - Marialessandra Contino
- Department of Pharmacy─Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy
| | - Nicola Corriero
- CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Michele Saviano
- CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | | | - Nicola Ancona
- CNR─Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
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