94 citations · 274 across the 10 of their papers we have counts for
10 papers
Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors
Simon Axelrod, Miroslav Kašpar, Kristýna Jelínková +8
Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set o…
Accurate Computation of Quantum Excited States with Neural Networks
David Pfau, Simon Axelrod, Halvard Sutterud +2
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states. Th…
Mapping the space of photoswitchable ligands and photodruggable proteins with computational modeling
Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli
Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and bio…
Learned Force Fields Are Ready For Ground State Catalyst Discovery
Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose +6
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery. Our key finding is that relaxation using forces fro…
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
Simon Axelrod, Eugene Shakhnovich, Rafael Gomez-Bombarelli
Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life…
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli
Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable s…
Molecular machine learning with conformer ensembles
Simon Axelrod, Rafael Gomez-Bombarelli
Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can lear…
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
Jurgis Ruza, Wujie Wang, Daniel Schwalbe-Koda +3
Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a p…
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod, Rafael Gomez-Bombarelli
Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D stru…
Differentiable Molecular Simulations for Control and Learning
Wujie Wang, Simon Axelrod, Rafael Gómez-Bombarelli
Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable both the elucidation of fundamental mechanisms and the engineering of matter for desired t…