The lead optimization stage of drug discovery---in which many derivatives of a low-affinity scaffold are synthesized and tested in an attempt to increase affinity and selectivity---is a crucial step in all small molecule drug discovery projects. Current approaches are driven by weak structure-activity relationships and (if structural biology support is available) rapid rounds of structural biology and molecular graphics, allowing slow and costly progress toward the goal of increased potency and minimal off-target toxicity. As a result, this process often takes many months and consumes a great deal of cost, both in terms of synthetic chemist salaries and reagent costs.
We have developed an enhanced sampling approach that allows us to explore combinatorially large spaces of inhibitor designs in a way that automatically biases the simulation toward inhibitors with higher affinity for one or more targets. This approach---based on expanded ensemble simulations and made possible by a new nonequilibrium Monte Carlo algorithm we developed---promises to provide a time- and cost-effective solution to the problem of optimizing small molecules for affinity. By restricting the space of compounds to those accessible from a given set of commercially-available starting materials and a library of common synthetic transformations, we aim to propose a set of compounds that have a high likelihood of increased potency and are likely to be readily synthesizable.
perses: Automated small molecule design by free energies [experimental]
Zhiqiang Tan (Rutgers): Stochastic approximation and Markov chain Monte Carlo theory
Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation
Jerome P. Nilmeier, Gavin E. Crooks, David D. L. Minh, and John D. Chodera.
Proc. Natl. Acad. Sci. USA 108:E1009, 2011. [DOI] [PDF]