Biography
Biography: Sergio Senar
Abstract
The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning approaches have been of special interest , since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds, and assessment of mechanism of action and identification of new targets for further research. .
Two public domains, NCBI and ChemBl hold millions of experimental records accessible to the whole biomedical community. The websites support applications to facilitate access to results for limited number of assays or molecules, but they also allow downloads of the database components, sometimes, of the database itself.
In this conference we will present an overview on some applications of ML techniques in construction of classification and/or prediction models of biological activity, identification of mechanism of action of molecules active in phenotypic assays and unveiling of potential new targets through pathways analysis. By carrying out a virtual screening on 1,.5M compounds, we´ll apply the results to the repurposing of marketed drugs to new therapies and match predicted drug activity to gene expression or occurrence of mutations. This last approach being of particular interest for the identification of combination of therapies in cancer.