4.6k citations · 31.4k across the 155 of their papers we have counts for
13 papers · 1 filter
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
Luca A. Thiede, Mario Krenn, AkshatKumar Nigam +1
Computer-aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning, and deep learning, in particular, have been topics where…
Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations
Cynthia Shen, Mario Krenn, Sagi Eppel +1
Computer-based de-novo design of functional molecules is one of the most prominent challenges in cheminformatics today. As a result, generative and evolutionary inverse designs fro…
Natural Evolutionary Strategies for Variational Quantum Computation
Abhinav Anand, Matthias Degroote, Alán Aspuru-Guzik
Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly-initialized…
Bayesian Variational Optimization for Combinatorial Spaces
Tony C. Wu, Daniel Flam-Shepherd, Alán Aspuru-Guzik
This paper focuses on Bayesian Optimization in combinatorial spaces. In many applications in the natural science. Broad applications include the study of molecules, proteins, DNA,…
Tequila: A platform for rapid development of quantum algorithms
Jakob S. Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza +15
Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are al…
Scientific intuition inspired by machine learning generated hypotheses
Pascal Friederich, Mario Krenn, Isaac Tamblyn +1
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in…