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De novo Molecule Design

De Novo Molecular Design

Discovery or designing of molecules with desired properties (for example: a molecule that binds to a protein of interest), is novel (i.e. no patent exists for this molecule) and satisfy drug-like properties such as solubility, bioavailability, non toxicity) is an effort intensive and time-consuming task. Moreover, even with such high efforts and time investment, the rate of success in the area of getting desirable molecules that succeed in a drug discovery pipeline is very limited.

At Innoplexus, we use an AI driven approach for generation of optimized molecular structures to facilitate drug discovery endeavor in the following ways:

  • Generation of molecular structures that can serve as new ideas/hypotheses towards modulating a drug target.
  • Optimization of molecular structures based on pre-existing molecules for a
    drug target.
  • Obtain new molecular structures with acceptable properties for moving into the drug discovery pipeline faster.

Our Deep-generative models leverage the existing molecular and biological data for training, along with cross-validation using scientific literature to provide valuable insights. In addition, we have also developed a molecule prioritization pipeline to evaluate the generated molecules based on: 1) Target protein binding potential, 2) Drug-like properties such as Lipinski’s rules, Quantitative Estimate of Drug-likeness (QED) and Synthetic Accessibility (SA), 3) ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties.

Thus, our pipeline can quickly generate and shuffle through millions of molecular candidates to not only identify the promising ones, but also learn with each iteration to create better molecules with acceptable drug-like properties thereby accelerating the drug discovery process.