Although the fundamental forces between atoms and molecules are almost fully understood at a theoretical level, and computer simulations have become an integral part of research activities, the application of these methods to large biomolecules still faces important practical difficulties due to the combinatorial explosion of possible interactions involved. Developing efficient protein structure prediction algorithms thus remains a major scientific challenge in computational biology. In my talk will demonstrate how machine learning and optimization in general can be used to design interaction potentials adapted to specific conformational space exploration problems. More precisely, I will present our recent results on the prediction of properties of small molecules [1], on the prediction of protein-protein [2, 3, 4] and protein-drug interactions [5, 6], as well as individual protein folds at atomic level. I will also present our methods for efficient space exploration with the derived knowledge-based potentials including a novel FFT-accelerated technique for protein-protein interactions…