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Models, Molecular

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Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models.

Journal of chemical information and modeling
Quantitative structure-property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they are associated with various shortcomings such as a l...

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps.

Journal of molecular biology
The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been th...

3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates.

Molecules (Basel, Switzerland)
Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is ...

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models.

Methods in enzymology
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray...

Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.

Journal of chemical information and modeling
Machine learning has become a crucial tool in drug discovery and chemistry at large, , to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs─pairs of molecules that are highly similar in their structure bu...

Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.

PLoS computational biology
Despite the immense progress recently witnessed in protein structure prediction, the modeling accuracy for proteins that lack sequence and/or structure homologs remains to be improved. We developed an open-source program, DeepFold, which integrates s...

Hallucinating symmetric protein assemblies.

Science (New York, N.Y.)
Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here, we use deep network hallucination to generate a wide range of symmetric protein homo-...

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.

Nature protocols
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-d...

Mapping Simulated Two-Dimensional Spectra to Molecular Models Using Machine Learning.

The journal of physical chemistry letters
Two-dimensional (2D) spectroscopy encodes molecular properties and dynamics into expansive spectral data sets. Translating these data into meaningful chemical insights is challenging because of the many ways chemical properties can influence the spec...

Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly.

Nature communications
Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-inten...