Technical Strengths

Computational Biology

Target assessment:

Availability of target protein structure is the key to understanding how the target functions and how it is modulated by small-molecules. We use a variety of techniques and tools developed in our laboratories for studying target protein dynamics and drugability. Elastic network model studies of proteins, for example, provide information on the global and functional motions of the target. We use these models to identify potential allosteric sites that may be suitable for allosteric modulation or inhibition of the target function. Also using molecular dynamics simulations in the presence of small organic solvent molecules, we characterize the binding preferences of target protein pockets and assess the maximal affinity that a drug molecule of a given size can achieve. These studies help us make rational decisions in investing on a particular target.



Many drugs exert their desired biological effects by interacting with multiple targets. In fact, analysis of drug-target databases shows that an approved drug interacts with an average of four targets. This raises the question whether polypharmacology is a necessary trait for efficacy of drugs. Phenotypic screens are naturally unbiased to any particular target, so they are able to discover compounds that may interact with a multitude of targets. To establish the basis for efficacy of compounds that we identify, we use cheminformatics and data-mining techniques to explore drug-target, activity, side-effect, and structural databases for target identification and side-effect prediction. By comparing HCS hit structures to compound structures and 3D shapes of annotated compounds and binding-sites of structurally resolved proteins, we identify potential targets. Testing and validation of these targets are done simply using their known modulators in the same phenotypic experimental setup. Once targets of hits are identified, we move on with computer-aided design and optimization of the compounds.


Systems Biology:

The Systems Biology approach to drug discovery augments traditional target-based approaches by providing information about cellular context. Our experts employ data-driven and rule-based modeling to investigate the effects of molecular entities on cellular pathway activity. Working in collaboration with the Department of Computational and Systems Biology, we are developing novel models and methods for studying systems biology on a variety of scales, ranging from single molecules to host-parasite interactions. We have at our disposal a variety of software designed specifically to identify mechanisms of allosteric regulation, predict protein-protein interactions, and construct and execute quantitative models of cellular pathways.



The ability to efficiently and accurately extract useful information from biological data allows us to maximize return on effort. Working in conjunction with other faculty of Biomolecular Informatics, we apply mathematical techniques such as probabilistic machine learning, artificial intelligence and Bayesian modeling to a variety of biological and pharamacological problems. Specific areas of focus include the analysis of combinatorial and statistical effects of drugs, predictive modeling of clinical outcome, data-based biomarker discovery and disease profiling, and translational bioinformatics.