Polypharmacology
Targeting the human interactome
De novo molecular design and the in silico prediction of polypharmacological profiles are emerging research topics poised to profoundly impact the future of drug discovery and precision medicine. We develop innovative computational techniques, including deep learning and generative AI, to understand complex drug effects and design bespoke molecules with precise desired properties.
Computer-assisted decision-making is now vital in medicinal chemistry. Moving beyond early single-target focus, high-quality data allows us to address complex interactions involving multiple ligands, binding sites, and receptors - the "interactome". This advance has established polypharmacology regulation as central to drug discovery, making the one target-one-drug approach often outdated.
Parallel advancements in computer science, including hardware, machine learning, and generative AI, are revolutionizing molecular informatics. These tools are essential for analyzing experimental data, from biophysical measurements to phenotypic compound screening, enabling new hypothesis generation. This convergence paves the way for chemogenomics-guided precision medicine, driven by advanced data analysis and the rational design of new chemical entities. Integrating modern computational concepts profoundly benefits drug discovery.