AIMC Journal:
Journal of chemical information and modeling

Showing 511 to 520 of 953 articles

Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks.

Journal of chemical information and modeling Jun 23, 2022
Assessing whether compounds penetrate the brain can become critical in drug discovery, either to prevent adverse events or to reach the biological target. Generally, pre-clinical in vivo studies measuring the ratio of brain and blood concentrations (...
Neural Networks, Computer Machine Learning Brain Drug Discovery
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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 ...
Drug Design Drug Discovery Deep Learning Models, Molecular
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Exploring Potential Energy Surfaces Using Reinforcement Machine Learning.

Journal of chemical information and modeling Jun 16, 2022
Reinforcement machine learning is implemented to survey a series of model potential energy surfaces and ultimately identify the global minima point. Through sophisticated reward function design, the introduction of an optimizing target, and incorpora...
Machine Learning Reward Reinforcement, Psychology
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Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides.

Journal of chemical information and modeling Jun 15, 2022
The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme...
Neural Networks, Computer Machine Learning Algorithms Peptides Amino Acids
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Predicting Protein-Ligand Docking Structure with Graph Neural Network.

Journal of chemical information and modeling Jun 14, 2022
Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both...
Neural Networks, Computer Molecular Docking Simulation Protein Binding Proteins Ligands
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Organic Compound Synthetic Accessibility Prediction Based on the Graph Attention Mechanism.

Journal of chemical information and modeling Jun 8, 2022
Accurate estimation of the synthetic accessibility of small molecules is needed in many phases of drug discovery. Several expert-crafted scoring methods and descriptor-based quantitative structure-activity relationship (QSAR) models have been develop...
Neural Networks, Computer Machine Learning Drug Discovery Bayes Theorem Quantitative Structure-Activity Relationship
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Retro Drug Design: From Target Properties to Molecular Structures.

Journal of chemical information and modeling Jun 2, 2022
To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods...
Humans Artificial Intelligence Machine Learning Drug Design Drug Discovery Molecular Structure
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Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast.

Journal of chemical information and modeling May 31, 2022
Deep learning has been a prevalence in computational chemistry and widely implemented in molecular property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL), has gathered growing attention for the potential ...
Neural Networks, Computer Machine Learning Cheminformatics Computational Chemistry
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IDL-PPBopt: A Strategy for Prediction and Optimization of Human Plasma Protein Binding of Compounds via an Interpretable Deep Learning Method.

Journal of chemical information and modeling May 24, 2022
The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting mor...
Humans Deep Learning Protein Binding Blood Proteins Chemistry, Pharmaceutical
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Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Journal of chemical information and modeling May 17, 2022
Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding...
Machine Learning Reproducibility of Results Molecular Docking Simulation Protein Binding Proteins Ligands
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