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Chemical Drug Development
Drug R&D is a very complicated, costly and time-consuming attempt. But with the advent of AI, this
process can be simplified and more quickly paced. AI system allows optimization of an algorithm to
identify new chemical matter with a desired molecular profile. AI has been used from the beginning of the
drug discovery process, including for initial hits from de novo design generated directly from data.
Chemical database (1060) is estimated by the
molecules containing up to 30 C, N, O and S
atoms, and it just a tiny fragment of all the
compounds with drug-like properties by
Chemical Source for Drug
7,895 drugs and experimental drugs
1.7 M bioactive small molecules from
~10 M small molecules for HTS
378 M commercially available compounds
from ZINC 15 (drug-likeness chemical
166 B enumerated small molecules up to 17
atoms (C, N, O, S and halogens)
Chemical Space of Drug-like Small Molecules in Theory
10 24 organic molecules with up to 30 atoms with known functional groups from P.Ertl, J,Chem. Inf.
Comput. Sci. 2003, 43, 374.
10 60 Drug-like small molecules up to MW of 500 from R. S. Bohacek, C. McMartin, W. C. Guida, Med. Res.
Rev. 1996, 16, 3.
Utilising AI to search a chemical space, which includes potentially billions of options in terms of atom
configuration, enabled the researchers to reduce the time taken to identify their target. Integrating data-
generating hypotheses with machine learning to produce drug design concepts, the steps previously
undertaken by humans were replaced with a suite of advanced algorithms.
AI-Driven Drug Discovery
Compared with traditional methods, MedAI, a division of MedAI, can help customers save the cost of
screening candidates by tens of billions every year. This AI platform can be widely used in various
scenarios regarding drug development.
Design novel leads Structure-based Drug Design