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patrick_aloy
02 Jan, 2020

Dr Patrick Aloy is an ICREA Research Professor and Principal Investigator of the Structural Bioinformatics and Network Biology lab at the IRB Barcelona. He has a BSc in Biochemistry and an MSc in Biotechnology from the Universitat Autònoma de Barcelona, Spain, and spent six years as postdoctoral researcher and staff scientist at the European Molecular Biology Laboratory, Heidelberg, Germany. For twenty years, Dr Aloy has been developing and implementing new technologies and algorithms, applying state-of-the-art methods to specific problems and bridging the gap between theoretical models and experiments in different disciplines. The main goal of his laboratory is to combine computational and structural biology with interaction discovery experiments to unveil the basic wiring architecture of physio-pathological pathways. It is his believe that a deeper knowledge of the global topology of interactome networks related to human disease will have important bearings in the discovery of new drug targets and biomarkers, optimization of preclinical models and understanding how biological networks change from the healthy state to disease. In the last years he has incorporated a complementary research line on systems pharmacology, so that the group can now tackle complex diseases from the drugs´ perspective. In particular, the SB&NB group has been developing resources to process, harmonize and integrate bioactivity data on small molecules, providing compound bioactivity descriptors that push the similarity principle beyond chemical properties, reaching various ambits of biology.

Title of keynote talk: Extending the small molecule similarity principle to all levels of biology

Large scale small molecule bioactivity data are not routinely integrated in daily biological research to the extent of other ‘omics’ information. Compound data are scattered and diverse, making them inaccessible to most researchers and not suited to standard statistical analyses. In this talk, I will present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profiles. In the CC, bioactivity data are expressed in a vector format, which naturally extends the notion of chemical similarity between compounds to similarities between bioactivity signatures of different kinds. We show how CC signatures can boost the performance of drug discovery tasks that typically capitalize on chemical descriptors, including compound library optimization, target identification and anticipation of failures in clinical trials. Moreover, we demonstrate and experimentally validate that CC signatures can be used to reverse and mimic biological signatures of disease models and genetic perturbations, options that are otherwise impossible using chemical information alone.