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Guided Multi-objective Generative AI to Enhance Structure-based Drug Design
The latent variables of the diffusion model are guided by differentiable scoring functions to explore uncharted chemical space and generate novel ligands in silico, optimizing a plurality of target physicochemical properties.
Machine Learning Diffusion Monte Carlo Energies
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total).
Twin Neural Network Regression
The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points.
Extensive deep neural networks for transferring small scale learning to large scale systems
We demonstrate the application of EDNNs to three physical systems: the Ising model and two hexagonal/graphene-like datasets.
Computational Physics Materials Science
