Gerard van Westen

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Full Professor AI & Medicinal Chemistry

Leiden University

Netherlands

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About

Gerard van Westen is a Full Professor of AI & Medicinal Chemistry at Universiteit Leiden in the Netherlands. His research focuses on the interaction of HLA-E/peptide complexes with immune receptors, the synthesis of novel drug design tools, and the application of computational methods to understand ligand binding and receptor function. He has contributed to recent publications exploring the optimization of drug candidates and the role of membrane proteins in cancer treatment.

Recent Grants

Grant: Open

OncodePACT

Open Date: 2023-01-01

Close Date: 2032-01-01

Grant: Close

DrugTRAIN

Open Date: 2021-01-01

Close Date: 2024-01-01

Grant: Close

AI for Computational Life Sciences

Open Date: 2021-01-01

Close Date: 2026-01-01

Grant: Close

Building an Artificial Intelligence Infrastructure for Mechanistic Pathology Understanding (MechPath)

Open Date: 2021-01-01

Close Date: 2023-01-01

Grant: Close

The Virtual Human Platform for Safety Assessment

Open Date: 2021-01-01

Close Date: 2026-01-01

Gerard van Westen

Research Interests

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About

Recent Grants

Articles (20)

IMERGE-FEP: Improving Relative Free Energy Calculation Convergence with Chemical Intermediates

HLA-E/peptide complexes differentially interact with NKG2A/CD94 and T cell receptors

Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design

N-Acyl-N-Alkyl Sulfonamide Probes for Ligand-Directed Covalent Labeling of GPCRs: The Adenosine A2B Receptor as Case Study

Exploring novel dilazep derivatives as hENT1 inhibitors and potentially covalent molecular tools

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

The effect of cancer-associated mutations on ligand binding and receptor function

AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models

Stereochemical optimization of N,2-substituted cycloalkylamines as norepinephrine reuptake inhibitors

CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides

Development of an Affinity-Based Probe to Profile Endogenous Human Adenosine A3 Receptor Expression

DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space

Large-Scale Modeling of Sparse Protein Kinase Activity Data

Artificial intelligence in multi-objective drug design

Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors

Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells

Hypoxia Triggers TAZ Phosphorylation in Basal A Triple Negative Breast Cancer Cells

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening

Impact of cancer-associated mutations in CC chemokine receptor 2 on receptor function and antagonism

Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors

Collaborators (11)

David Mobley

Willem Jespers

Peter Steinberger

Jan Wouter Drijfhout

Sylvia Emmanuelle Le Dévédec

Ad IJzerman

Olivier JM BÉQUIGNON

erik danen

Martijn Riool

Alzbeta Tuerkova

Barbara Zdrazil

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