Christian Tyrchan

1. Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
   2017/01/05 by Marwin Segler, Marwin H. S. Segler, Segler, Marwin H. S. +6 · 3 voices · 1 citation
   Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · Mathematics · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Neural and Evolutionary Computing (cs.NE) #Protein Structure and Dynamics #cs.AI #cs.LG #cs.NE #physics.chem-ph #stat.ML
2. Randomized SMILES strings improve the quality of molecular generative models
   2019/11/21 by Josep Arús‐Pous, Simon Johansson, Oleksii Prykhodko +5 · 1 voice · 13 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #Gene expression and cancer classification
3. Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites
   2024/11/01 by Gustav Olanders, Giulia Testa, Alessandro Tibo +2 · 1 voice · 3 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · #Protein Structure and Dynamics #Computational Drug Discovery Methods #Microbial Metabolic Engineering and Bioproduction
4. Diversity-Aware Reinforcement Learning for de novo Drug Design
   2024/10/14 by Hampus Gummesson Svensson, Christian Tyrchan, Svensson, Hampus Gummesson +5 · 2 citations
   Computer Science · #Advanced Multi-Objective Optimization Algorithms #Biomolecules (q-bio.BM) #Computational Drug Discovery Methods #Evolutionary Algorithms and Applications #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG)