Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis.

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives.

A collaborative database and computational models for tuberculosis drug discovery.

The search for molecules with activity against Mycobacterium tuberculosis (Mtb) is employing many approaches in parallel including high throughput screening and computational methods. We have developed a database (CDD TB) to capture public and private Mtb data while enabling data mining and collaborations with other researchers. We have used the public data along with several cheminformatics approaches to produce models that describe active and inactive compounds.

Stochastic innovation as a mechanism by which catalysts might self-assemble into chemical reaction networks.

We develop a computer model for how two different chemical catalysts in solution, A and B, could be driven to form AB complexes, based on the concentration gradients of a substrate or product that they share in common. If A's product is B's substrate, B will be attracted to A, mediated by a common resource that is not otherwise plentiful in the environment. By this simple physicochemical mechanism, chemical reactions could spontaneously associate to become chained together in solution.