Centre for Computational Life Sciences (CCLS)
CCLS since 2018
The Centre for Computational Life Sciences is a young and energetic community.
The Centre for Computational Life Sciences was formed in 2018 by Michael Emmerich and Gerard van Westen.
Since then monthly meetings and one day event have been successfully organised. Participants come from the different institutes of the Science Department of Leiden University.
Gerard van Westen
I am Gerard van Westen, Professor of Artificial Intelligence & Medicinal Chemistry, currently working at Leiden University (the Netherlands) as PI in computational drug discovery. My main lines of research are on the one hand the application of various machine learning methods in pre-clinical and early-clinical drug discovery, and on the other hand the application of structure-based drug discovery methods. Currently I have 13 years of experience in this area and my interests closely align with the Center for Computational Life Sciences.
In my group 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, but we also use images as input (based on high-throughput microscopy) for these models. Finally, we use generative neural networks 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.
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 returned to Leiden to become PI Computational Drug Discovery at Leiden University. 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.
Michael T. M. Emmerich is an Associate Professor at LIACS, Leiden University, and leader of the Multicriteria Optimization and Decision Analysis (MODA) research group and together with Gerard van Westen scientific coordinator of the Leiden Center for Computational Life Sciences (CCLS). Moreover, he is a guest researcher within the Decision Analytics Utilizing Causal Models and Multiobjective Optimization (DEMO) thematic group at Jyväskylä University, Finland. Additionally, he is Visiting Professor at the Department of Electronics and Information Technology, Lviv Polytechnic National University, Lviv, Ukraine
He was born in Coesfeld, Germany and received his doctorate in 2005 from TU Dortmund, Germany (H.-P. Schwefel, P. Buchholz promoters). He carried out projects as a researcher with the German Chemical Industry, TU Dortmund, RWTH Aachen, IST Lisbon, University of the Algarve (Portugal), ACCESS Material Science e.V. (Germany), and the FOM/AMOLF institute on Fundamental Science of Matter (Netherlands). Since 2005 he is affiliated with Leiden University and since 2019 also with Jyväskylä University. He is known for pioneering work on model-assisted and indicator-based multiobjective optimization, complex networks, and machine learning, and has edited 5 books, 1 monography, and co-authored more than 200 papers on multiobjective optimization and machine learning algorithms and their application in computational bio-chemistry, computer-aided design, and chemical engineering.
Lu Cao is an Assistant Professor in the Imaging and Bioinformatics Lab, LIACS, Leiden University. She is specialized in designing automatic image analysis systems especially for high-throughput screening. She is dedicated to facilitate the study of disease mechanism and drug discovery using automatic image analysis and data analysis scheme.
Currently, she is closely working with the imaging group with several projects. One of the projects focuses on classification of Pollen Images using deep learning models together with collaborators from Naturalis Biodiversity Center. She also continues her Postdoc project: Cardiotoxicity study to identify disrupted sarcomere structures of iPSC-Cardiomyocytes from hundreds of thousands of microscope images with partners from Twente University. Furthermore, she has a close tie with some bio-tech industries. She worked two years to increase the throughput of Calcisum and Contractility Measurement System by designing an automatic cell finding pipeline for Cytocypher B.V.. Now, she is setting up further collaboration with the company to improve the cell finding performance using deep learning model in Multicell High Throughput system.