Mestres, Jordi

1. Fragment-Based Drug Discovery

Fragment-Based Drug Discovery (FBDD) is a powerful strategy with a proven track record of generating potent bioactive small molecules from low-affinity chemical fragments. Computational approaches to FBDD are often limited by the availability of high-quality, structurally resolved data on fragment binding poses. To address this gap, we have developed a Structurally Augmented Fragment Repository (SAFR), a novel data set designed to support in silico FBDD. Initially, a set of 89,375 high-confident binding poses of bioactive molecules in public sources was obtained by applying a filtering protocol involving 2D ligand similarity and 3D ligand superposition against protein-bound ligand structures followed by scoring with protein–ligand docking and interaction features. Fragmentation of the bioactive ligands in their predicted binding poses resulted in a total of 818,385 fragment-protein interactions between 157,080 unique chemical fragments and environments from 1,142 distinct proteins. Of them, 270,155 are unique fragment-protein interactions, of which 237,284 (88%) are not represented in protein-bound ligands in the PDB. Case studies using SAFR for bioisosteric replacements and scaffold hopping are presented. SAFR is a useful resource to support fragment screening campaigns and hit-to-lead optimization.

It is publicly available at https://zenodo.org/records/18229523.
Recent publication: https://doi.org/10.1021/acs.jcim.6c00217.

2. Generative Chemistry

Efficient exploration of chemical space is an essential component of modern generative drug design. We have developed ChemBang, a computational engine that grows small molecules based on chemical transformations extracted by matched molecular pair analysis of all structures available in catalogues of synthesized molecules. Each chemical transformation is mapped onto its associated atomic environment defined as the substructure within a three-atom radius from the transformation site. Unsupervised chemical evolution is then performed in cycles by systematically applying chemical transformations to all exposed atomic environments present in a seed structure. Multiple physicochemical properties and substructural alerts are incorporated to effectively guide the generation of drug-like synthetically accessible molecules. As a use case, the generation of the Erdafitinib structure from any of its three ring systems (pyrazole, benzene and quinoxaline), and the evolution of the property distributions from all molecules generated in each cycle, are discussed in detail. The ability to explore the chemical space of pharmaceutical relevance is shown by successfully generating the exact chemical structure of 95.3% of all 2,809 small-molecule ATC drugs from their constituting fragments.

Recent publication: https://doi.org/10.1002/minf.70036

3. ML-based Safety Models

Modern generative chemistry initiatives aim to produce potent and selective novel synthetically feasible molecules with suitable pharmacokinetic properties. General ranges of physicochemical properties relevant for the absorption, distribution, metabolism, and excretion (ADME) of drugs have been used for decades. However, the therapeutic indication, dosing route, and pharmacodynamic response of the individual drug discovery program may ultimately define a distinct desired property profile. A methodological pipeline to build and validate machine learning (ML) models on physicochemical and ADME properties of small molecules is introduced. The analysis of publicly available data on several ADME properties presented in this work reveals significant differences in the property value distributions across the various levels of the anatomical, therapeutic, and chemical (ATC) drug classification. For most properties, the predicted data distributions agree well with the corresponding distributions derived from experimental data across fourteen drug classes. The refined ADME profiles for ATC drug classes should be useful to guide the de novo generation of advanced lead structures directed toward specific therapeutic indications.

Recent publication: https://doi.org/10.3390/pharmaceutics17030308

4. Safety Signal Detection and Analysis

External factors severely affecting in a short period of time the spontaneous reporting of adverse events (AEs) can significantly impact drug safety signal detection. Coronavirus disease 2019 (COVID-19) represented an enormous challenge for health systems, with over 767 million cases and massive vaccination campaigns involving over 70% of the worldwide population. This study investigates the potential masking effect on certain AEs caused by the substantial increase in reports solely related to COVID-19 vaccines within various spontaneous reporting systems (SRSs). Three SRSs were used to monitor AEs reporting before and during the pandemic, namely, the World Health Organisation (WHO) global individual case safety reports database (VigiBase®), the United States Food and Drug Administration Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report database (JADER). Findings revealed a sudden over-reporting of 35 AEs (? 200%) during the pandemic, with an increment of the RRF value in 2021 of at least double the RRF reported in 2020. This translates into a substantial reduction in signals of disproportionate reporting (SDR) due to the massive inclusion of COVID-19 vaccine reports. To mitigate the masking effect of COVID-19 vaccines in post-marketing SRS analyses, we recommend utilizing COVID-19-corrected versions for a more accurate assessment.

Recent publication: https://doi.org/10.1038/s41598-023-46275-w

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Prof. Tudor I. Oprea – Dompé Farmaceutici, Italy
Prof. Tero Aittokallio – University of Helsinki, Finland
Prof. Gianluca Trifirò – University of Verona, Italy
Prof. Vladimir Poroikov – Russian Academy of Sciences, Russia
Dr. Julia Santiago – University of Lausanne, Switzerland
Dr. José A. Márquez – European Molecular Biology Laboratory (EMBL), France
Dr. Isabelle Vernos – Center for Genomic Regulation (CRG), Spain
Dr. Lluís Ribas de Pouplana – Institute for Research in Biomedicine (IRB), Spain
Dr. Lluís Espinosa – Hospital del Mar Research Institute (IMIM), Spain
Dr. Pilar Navarro – Superior Council of Scientific Investigations (CSIC), Spain
Dr. Anna Pla-Quintana – University of Girona, Spain