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Bayesian inference in computational chemistry


Most drug discovery projects have a 95% failure rate. In this talk I will show how I used Pymc, a Python probabilistic programming language, to improve molecular models used in the early stages of drug discovery to eliminate unlikely drug candidates. In addition, I will discuss how I propagated model error using reweighing techniques.


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