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R&D collaboration building on Lead Pharma’s expertise in the discovery, design and optimization of smallmolecule treatments.
Our rigorous target selection process, translational screening cascade, and smart medicinal chemistry have been essential to bring this project to this stage.
Data science has emerged as an innovative tool in the biopharmaceutical industry, leveraging the power of machine learning and artificial intelligence to drive innovation and efficiency across the entire drug development lifecycle. These complex molecules require precise engineering to ensure optimal efficacy and safety.
The mission of the CCDD is to discover novel small-molecule therapeutics for the treatment of cancer and progress them to hypothesis testing phase 1 clinical trials. For example, in the screening stage of a project we are trying to find initial starting points for optimisation by our medicinal and computationalchemistry colleagues.
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The premise at the time was that putting computationalchemistry in the primary position for new molecule ideation would upend the drug discovery paradigm. set up a “virtual project team,” leverage Schrödinger scientists to lead computationalchemistry, and do all the wet work at CROs. It did just that.
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