We design and apply protocols and computational algorithms to gain insight into biological and chemical systems with pharmacological importance and use this knowledge to rationally design and repurpose new potential therapeutic agents that can contribute to the treatment of human diseases. We use a vast range of methods, such as virtual screening, docking, homology and pharmacophore modeling, molecular dynamics, quantum chemistry, cheminformatics and machine learning.
Our lab has a broad background on state-of the-art tools of computational (bio)chemistry and cheminformatics to address challenging pharmaceutical and chemical questions in drug discovery. We are also committed to developing data-driven analysis methods (AI/ML/Data Science/Big Data) and their application in chemical biology and drug discovery.
Our lab focuses mainly on targeting cancer, neurodegenerative and infectious diseases, and works closely with experimental collaborators as well as pharmaceutical companies.
Computer-assisted Hit Discovery and Lead Optimization
We identify (or repurpose) small molecules that modulate protein activity and polypharmacology (e.g. Proteasome, EZH2, PD-L1, HK2, RIP1, others) based on the 3D structure of the targets and/or ligands using VS, docking, pharmacophore modeling, and other in-house developed consensus protocols (e.g. binding sites analysis) and ML.
Optimization of chemical reactions to produce API’s
We use quantum chemical methods (DFT calculations) to explain and predict the formation of impurities and to improve efficacy in chemical reactions to produce API’s. These calculations are critical to elucidate reaction mechanisms and provide data to design new reactions, reagents, and catalysts.
We use different published tools as well as in-house machine learning models, signaling network data as well as pathway enrichment analysis to carry out target fishing to elucidate readouts produced by phenotypic screening.
Development of user-friendly tools
We have created the iChem4Screen platform which works as a repository of data produced within iMed.ULisboa. We are also developing a tool to facilitate automated curation of raw PDB data.