Filipe Menezes

Local structural preference maps encode transferable protein-interface energetics

The paper trains a machine-learning model on native protein structures to predict interaction preferences and energetics without using affinity or mutation labels, achieving performance comparable to existing methods on a benchmark.

It is Not Always Crystal Clear: In-Pocket Analysis for Understanding Binding Site Microenvironment Properties

The paper introduces In-Pocket, a computational method that validates and improves the chemical accuracy of protein-ligand binding interfaces using quantum mechanics and experimental data.

Target Preference Maps: A machine learning model generalizing transferable drug-receptor interactions and guiding drug discovery

The paper introduces a machine learning model that learns spatial preference maps for drug-receptor interactions to guide drug discovery.

Curcumin Inhibition of DYRK Kinases

This paper investigates how curcumin interacts with DYRK1A and DYRK2 kinases, showing its potential to treat diabetes and cancer by modulating these proteins.

MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery

The paper introduces MISATO, a dataset combining quantum mechanics properties and molecular dynamics simulations of protein-ligand complexes to support structure-based drug discovery.

Quantum Chemistry in a Pocket: A Multifaceted Tool to Link Structure and Activity

The paper introduces an algorithm for optimizing protein-ligand complex structures, improving data curation and analysis for drug discovery.

Can Quantum Chemistry Improve the understanding of Protein-Ligand Interactions? Implications for Structure Based Drug Discovery

The paper introduces an Energy Decomposition Analysis to understand non-covalent binding in drug-protein complexes, validated against experimental data for PEX14-PEX5 and its inhibitors.