AIMC Topic: Models, Molecular

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Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.

Journal of chemical information and modeling Dec 1, 2022
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...
Machine Learning Structure-Activity Relationship Models, Molecular Algorithms Drug Discovery
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Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.

PLoS computational biology Sep 16, 2022
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...
Protein Conformation Protein Folding Databases, Protein Computational Biology Models, Molecular Proteins Software Algorithms Deep Learning
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Hallucinating symmetric protein assemblies.

Science (New York, N.Y.) Sep 15, 2022
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-...
Protein Subunits Cryoelectron Microscopy Models, Molecular Biocompatible Materials Protein Conformation Protein Engineering Deep Learning
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I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.

Nature protocols Aug 5, 2022
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...
Protein Conformation Cryoelectron Microscopy Models, Molecular Deep Learning Computational Biology Proteins Databases, Protein Algorithms
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Mapping Simulated Two-Dimensional Spectra to Molecular Models Using Machine Learning.

The journal of physical chemistry letters Aug 5, 2022
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...
Machine Learning Models, Molecular Spectrum Analysis Vibration
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Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly.

Nature communications Jul 13, 2022
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...
Cryoelectron Microscopy Models, Molecular Protein Conformation Reproducibility of Results Deep Learning
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Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions.

Journal of chemical information and modeling Jul 6, 2022
In recent years, molecular deep generative models have attracted much attention for its application in drug design. The data-driven molecular deep generative model approximates the high dimensional distribution of the chemical space through learning...
Proteins Models, Molecular Ligands Neural Networks, Computer Drug Design
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Explainable machine learning for medicinal chemistry: exploring multi-target compounds.

Future medicinal chemistry Jul 1, 2022
Machine Learning Chemistry, Pharmaceutical Models, Molecular
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Factor analysis of error in oxidation potential calculation: A machine learning study.

Journal of computational chemistry Jun 28, 2022
The conductor-like polarizable continuum model (C-PCM), which is a low-cost solvation model, cannot treat characteristic interactions between the solvent and substructure(s) of the solute. Moreover, the error in a charged system is significant. Using...
Solvents Solutions Thermodynamics Factor Analysis, Statistical Machine Learning Models, Molecular
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Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation.

Journal of chemical information and modeling Jun 17, 2022
Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still a challenging task in current drug discovery. In this study, we developed a conditional generative ...
Deep Learning Models, Molecular Drug Design Drug Discovery
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