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-Universe-1 Chemical Universe

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 logarithmic scale.

Chemical-Universe-2 Chemical Source for Drug Research

  • 7,895 drugs and experimental drugs
  • 1.7 M bioactive small molecules from ChemBL
  • ~10 M small molecules for HTS
  • 378 M commercially available compounds from ZINC 15 (drug-likeness chemical space)
  • 166 B enumerated small molecules up to 17 atoms (C, N, O, S and halogens)

Chemical-Universe-3 Chemical Space of Drug-like Small Molecules in Theory

  • 1024 organic molecules with up to 30 atoms with known functional groups from P.Ertl, J,Chem. Inf. Comput. Sci. 2003, 43, 374.
  • 1060 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.

Chemical-Universe-4 AI-Driven Drug Discovery

Compared with traditional methods, AI & Medicine, a division of Protheragen, 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.

Chemical-Universe-5

Chemical-Universe-6 Computational Approaches to Structure-based Drug Discovery

  • Chemical-Universe-7 Design novel leads (de novo drug design)
  • Chemical-Universe-8 Identify interaction sites
  • Fragment fitting Fragment fitting
  • Bridging Bridging

nomain-title-log-pic1 Screen hits from existing compound library (virtual screening)

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  • nomain-title-log-pic1 Molecular Generation and Filtering
  • nomain-title-log-pic2 Sampling enough examples from the pretrain model (remove duplicates in iterations)
  • nomain-title-log-pic2 Filtering by -2<logP<6, 350<MW<750, HBD<5, HBA<10, TPSA<150, NRB<10.
  • nomain-title-log-pic2 Removing molecules which are not pass MCF.
  • nomain-title-log-pic2 Removing molecules that are similar to those in patent. (Tc>0.85)
  • nomain-title-log-pic2 Removing molecules that are similar to each other. (Tc>0.85)
  • nomain-title-log-pic2 Removing molecules that contain scaffold present in the patent.
  • nomain-title-log-pic2 Adding synthetic accessibility(SA) and Quantitative Estimate of Drug-likeness(QED).
  • nomain-title-log-pic2 Retaining molecules that are specific in interaction with target.
  • nomain-title-log-pic2 Manual selection by medicinal chemist.

nomain-drag-pic1 Timeline

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nomain-drag-pic1 Results and Delivery

  • nomain-title-log-pic2 Molecules designed by AI exhibit reasonable quality: they are drug-like, synthesizable and within the chemical space of specific target.
  • nomain-title-log-pic2 The chemical candidates designed by AI exhibit potent bioactivity against specific targets.
  • nomain-title-log-pic2 The total amount of time for drug design is about 4-6 weeks, including data collection, modeling building, molecular generation and selection.

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