Draft:Molecular intelligence
| Submission declined on 20 January 2024 by Ldm1954 (talk). This submission appears to read more like an advertisement than an entry in an encyclopedia. Encyclopedia articles need to be written from a neutral point of view, and should refer to a range of independent, reliable, published sources, not just to materials produced by the creator of the subject being discussed. This is important so that the article can meet Wikipedia's verifiability policy and the notability of the subject can be established. If you still feel that this subject is worthy of inclusion in Wikipedia, please rewrite your submission to comply with these policies.
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Comment: A Google search on "Molecular intelligence" pulls up a large number of hits for the product "Caris molecular intelligence", and little more. I feel this is a stealth advert. It also does not meet notability. Ldm1954 (talk) 03:06, 20 January 2024 (UTC)
Molecular intelligence is an interdisciplinary field of science that combines molecular biology principles with artificial intelligence and computational biology.
It focuses on the understanding and manipulation of molecular structures and interactions for applications, especially in biotechnology and pharmaceutical research. In this area, computational tools, AI algorithms and big data analysis are used to predict the behavior of molecules, develop drugs and simulate biological processes. Key technologies include bioinformatics and molecular modeling.
The main goal of molecular intelligence is to accelerate scientific discoveries and improve the efficacy and precision of therapeutic interventions and biotechnological processes. In this field, computer-aided tools, AI algorithms and extensive data analysis are used to predict the behavior of molecules, develop drugs and simulate biological processes.
Key applications are drug discovery[1], enzyme-driven systems[2] and protein-based nano-machines. The integration of these technologies enables researchers to analyze large amounts of data of complete genomes and make predictions about the behavior of molecules and biological systems.
In drug research, molecular intelligence plays a crucial role in the identification and development of new drugs, for off-target analysis, the in-silico prediction of adverse side-effects and repurposing. By using AI algorithms and computer models, researchers can identify potential drug candidates and predict their efficacy and safety. Molecular intelligence also plays an important role in the analysis of enzyme activities, which are important for the development of new biotechnological processes.
Companies active in this area include BenevolentAI, which uses AI to improve drug discovery and development, Innophore, which specializes in AI-powered molecular analysis with a point-cloud based Catalophore technology, Atomwise, which focuses on AI for drug discovery, particularly on predicting drug-target interactions, Recursion Pharmaceuticals, which combines experimental biology with AI to unravel biological complexity and accelerate drug discovery, and Schrodinger, which provides software solutions and services for computational chemistry, including drug discovery.
Recent applications of molecular intelligence-based methods include predicting the binding affinity of SARS-CoV-2 variants to human cells[3], repurposing existing drugs for novel applications[4], genome-wide analysis of potential drug targets for emerging pathogens[5] or predicting massive protein-ligand interactions involving 36 billion chemical compounds.
References[edit]
- ^ "AI's potential to accelerate drug discovery needs a reality check". Nature. 622 (7982): 217. 2023-10-10. Bibcode:2023Natur.622..217.. doi:10.1038/d41586-023-03172-6. PMID 37817040.
- ^ Markus, Braun; C, Gruber Christian; Andreas, Krassnigg; Arkadij, Kummer; Stefan, Lutz; Gustav, Oberdorfer; Elina, Siirola; Radka, Snajdrova (2023-11-03). "Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design". ACS Catalysis. 13 (21): 14454–14469. doi:10.1021/acscatal.3c03417. ISSN 2155-5435. PMC 10629211. PMID 37942268.
- ^ Köchl, Katharina; Schopper, Tobias; Durmaz, Vedat; Parigger, Lena; Singh, Amit; Krassnigg, Andreas; Cespugli, Marco; Wu, Wei; Yang, Xiaoli; Zhang, Yanchong; Wang, Welson Wen-Shang; Selluski, Crystal; Zhao, Tiehan; Zhang, Xin; Bai, Caihong (2023-01-14). "Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations". Scientific Reports. 13 (1): 774. Bibcode:2023NatSR..13..774K. doi:10.1038/s41598-023-27636-x. ISSN 2045-2322. PMC 9840421. PMID 36641503.
- ^ Hetmann, M.; Langner, C.; Durmaz, V.; Cespugli, M.; Köchl, K.; Krassnigg, A.; Blaschitz, K.; Groiss, S.; Loibner, M.; Ruau, D.; Zatloukal, K.; Gruber, K.; Steinkellner, G.; Gruber, C. C. (2023-07-21). "Identification and validation of fusidic acid and flufenamic acid as inhibitors of SARS-CoV-2 replication using DrugSolver CavitomiX". Scientific Reports. 13 (1): 11783. Bibcode:2023NatSR..1311783H. doi:10.1038/s41598-023-39071-z. ISSN 2045-2322. PMC 10362000. PMID 37479788.
- ^ Parigger, Lena; Krassnigg, Andreas; Grabuschnig, Stefan; Gruber, Karl; Steinkellner, Georg; Gruber, Christian C. (2023-12-12). Chang, Wen (ed.). "AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak". Microbiology Spectrum. 11 (6): e0231523. doi:10.1128/spectrum.02315-23. ISSN 2165-0497. PMC 10714838. PMID 37874150.
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