Hi, I am Gerard van Westen. Welcome to my own personal webspace. In my group (computational drug discovery ( www.cddl.nl )) we use artificial intelligence approaches in various areas. Examples include the use of machine learning (regressors) to predict affinity of drug candidates (molecules) for drug targets (proteins) in a polypharmacological context. Input for these models are properties of the molecule (chemical), properties of the protein (e.g. binding site), and properties of the interaction (e.g. interaction type). Furthermore we use classifiers in a similar way to predict activity or toxicity of drug candidates, however for this we also use images as input (based on high-throughput microscopy). Finally we use generators to generate novel drug-candidates (using a sequential SMILES format) that meet one or more predefined criteria. In all these cases we use experimental validation to validate our models. For this we collaborate heavily with experimental groups (medicinal chemistry, chemical biology).
Recently we have started exploring the distribution of machine learning models as tools for chemists. Our models are wrapped and made accessible for chemist to use in day to day routines as part of their work. We aim to make AI a low threshold tool that can be used to speed up and improve the classical medicinal chemistry.
Currently I have 13 years of experience in the application of computational drug discovery. My MSc was in Biopharmaceutical Sciences but during my MSc (upto 2007) and PhD (upto 2013) I specialized in the application of machine learning in drug discovery. I obtained my PhD (Leiden University) on a grant sponsored by Janssen Pharmaceutica. Subsequently I did a postdoc at the European Bioinformatics Institute in Cambridge (UK) on a Marie Curie / EMBL fellowship. I currently collaborate with various commercial and non-commercial organizations and within the EU I am active in both the EUToxRisk and eTRANSAFE IMI consortia.
I am always interested in collaborations, you can email me at