AI Medical Compendium Journal:
Journal of computer-aided molecular design

Showing 21 to 30 of 67 articles

Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization.

Journal of computer-aided molecular design
Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from ph...

Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.

Journal of computer-aided molecular design
Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed...

Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.

Journal of computer-aided molecular design
The retrospective evaluation of virtual screening approaches and activity prediction models are important for methodological development. However, for fair comparison, evaluation data sets must be carefully prepared. In this research, we compiled str...

Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.

Journal of computer-aided molecular design
Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engine...

Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery.

Journal of computer-aided molecular design
The support vector machine (SVM) algorithm is one of the most widely used machine learning (ML) methods for predicting active compounds and molecular properties. In chemoinformatics and drug discovery, SVM has been a state-of-the-art ML approach for ...

Turbo prediction: a new approach for bioactivity prediction.

Journal of computer-aided molecular design
Nowadays, activity prediction is key to understanding the mechanism-of-action of active structures discovered from phenotypic screening or found in natural products. Machine learning is currently one of the most important and rapidly evolving topics ...

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction.

Journal of computer-aided molecular design
We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN...

Imputation of sensory properties using deep learning.

Journal of computer-aided molecular design
Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the appl...

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.

Journal of computer-aided molecular design
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-re...

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.

Journal of computer-aided molecular design
Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often...