Graph Neural Networks (GNNs) are powerful tools for predicting chemical properties, but their black-box nature can limit trust and utility. Explainability through feature attribution and awareness of ...

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs n...

Electron density is a fundamental quantity that can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a real-space grid have been proposed, the demand for a number of high-order basis functions or grid points is enormous. In this work, we propose an efficient grid-point sampling strategy that combines targeted sampling favoring a large density and a screening of grid points associated with linearly independent atomic features. This new sampling strategy is integrated with a field-induced recursively embedded atom neural network model to develop a real-space grid-based ML model for the electron density and its response to an electric field. This approach is applied to a QM9 molecular data set, a H2O/Pt(111) interfacial system, an Au(100) electrode, and an Au nanoparticle under an electric field. The number of training points is found to be much smaller than previous models, while yielding comparably accurate predictions for the electron density of the entire grid. The resultant machine-learned electron density model enables us to properly partition partial charge onto each atom and analyze the charge variation upon proton transfer in the H2O/Pt(111) system. The machine-learning electronic response model allows us to predict charge transfer and the electrostatic potential change induced by an electric field applied to an Au(100) electrode or an Au nanoparticle.

Darwinian evolution of self-replicating entities most likely played a key role in the emergence of life from inanimate matter. For evolution to occur,…

Colab Notebooks covering deep learning tools for biomolecular structure prediction and design - Graylab/DL4Proteins-notebooks

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, ef...

Kresten Lindorff-Larsen, Professor at the Linderstrøm-Lang Centre for Protein Science in the De­partment of Biology, University of Copenhagen, studied biochemistry, leading him to...

We present the NetSci program–an open-source scientific software package designed for estimating mutual information (MI) between data sets using GPU acceleration and a k-nearest-neighbor algorithm. This approach significantly enhances calculation speed, achieving improvements of several orders of magnitude over traditional CPU-based methods, with data set size limits dictated only by available hardware. To validate NetSci, we accurately compute MI for an analytically verifiable two-dimensional Gaussian distribution and replicate the generalized correlation (GC) analysis previously conducted on the B1 domain of protein G. We also apply NetSci to molecular dynamics simulations of the Sarcoendoplasmic Reticulum Calcium-ATPase (SERCA) pump, exploring the allosteric mechanisms and pathways influenced by ATP and 2′-deoxy-ATP (dATP) binding. Our analysis reveals distinct allosteric effects induced by ATP compared to dATP, with predicted information pathways from the bound nucleotide to the calcium-binding domain differing based on the nucleotide involved. NetSci proves to be a valuable tool for estimating MI and GC in various data sets and is particularly effective for analyzing intraprotein communication and information transfer.