This position will investigate the application of a range of supervised learning techniques, representations and data augmentation strategies to the discovery of bioactive molecules in ultra-large libraries for selected therapeutic targets. The project will exploit large volumes of protein structure data, including recently available Alphafold2 structures. Selection criteria - Essential • A PhD awarded in an area relevant to the project. The CRCM is also affiliated to the private cancer hospital Institut Paoli Calmettes, the CNRS and Aix-Marseille University. The successful candidate will have a 3-year contract with a gross monthly salary of up to €2,900 gross monthly (depending on experience).

In the COVID era, computational biology is having a heyday – and machine learning is playing a massive role. With billions upon billions of compounds to search through for any given therapeutic application, strictly brute-force simulations are wildly unfeasible, necessitating more artificially intelligent methods of whittling down the options. Now, researchers from IRB Barcelona's Structural Bioinformatics and Network Biology lab have developed a deep learning method that predicts the biological activity of any given molecule – even in the absence of experimental data. The researchers, led by Patrick Aloy, are applying deep machine learning to a massive dataset: the Chemical Checker, which provides processed, harmonized, and integrated bioactivity data on 800,000 small molecules and is also produced by the Structural Bioinformatics and Network Biology lab. In total, any given molecule has 25 bioactivity "spaces," but for most molecules, data on only a few are known – if that.