AI Medical Compendium Journal:
Journal of chemical information and modeling

Showing 21 to 30 of 934 articles

Machine Learning of Molecular Dynamics Simulations Provides Insights into the Modulation of Viral Capsid Assembly.

Journal of chemical information and modeling
An effective approach in the development of novel antivirals is to target the assembly of viral capsids by using capsid assembly modulators (CAMs). CAMs targeting hepatitis B virus (HBV) have two major modes of function: they can either accelerate nu...

Machine Learning Prediction of Optical Properties of Coumarin Derivatives Using Gaussian-Weighted Graph Convolution and Subgraph Modular Input.

Journal of chemical information and modeling
Coumarin derivatives have been widely developed and utilized as chromophores and fluorophores in various research fields. In this study, we constructed an experimental database of the optical properties─specifically, absorption and emission wavelengt...

Multi-Knowledge Graph and Multi-View Entity Feature Learning for Predicting Drug-Related Side Effects.

Journal of chemical information and modeling
Computational prediction of potential drug side effects plays a crucial role in reducing health risks for clinical patients and accelerating drug development. Recent methods have constructed heterogeneous graphs that represent drugs and their side ef...

Atomic Energy Accuracy of Neural Network Potentials: Harnessing Pretraining and Transfer Learning.

Journal of chemical information and modeling
Machine learning-based interatomic potentials (MLIPs) have transformed the prediction of potential energy surfaces (PESs), achieving accuracy comparable to calculations. However, atomic energy predictions, often assumed to lack physical meaning, rem...

Machine Learning Classification of Chirality and Optical Rotation Using a Simple One-Hot Encoded Cartesian Coordinate Molecular Representation.

Journal of chemical information and modeling
Absolute stereochemical configurations and optical rotations were computed for 121,416 molecular structures from the QM9 quantum chemistry data set using density functional theory. A representation for the molecules was developed using Cartesian coor...

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation.

Journal of chemical information and modeling
Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pretraining data, and adversarial training on a BERT-based model for yield pr...

SF-Rx: A Multioutput Deep Neural Network-Based Framework Predicting Drug-Drug Interaction under Realistic Conditions for Safe Prescription.

Journal of chemical information and modeling
Drug-drug interaction (DDI) can compromise therapeutic efficacy and cause detrimental effects in polypharmacy. Computational prediction of DDI has emerged as an alternative approach to time-consuming clinical experiments for investigating potential d...

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.

Journal of chemical information and modeling
Today's drug discovery increasingly relies on computational and machine learning approaches to identify novel candidates, yet data scarcity remains a significant challenge. To address this limitation, we present , an application specifically designed...

Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties?

Journal of chemical information and modeling
The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance of quantifying molecular similarity. Molecular similarity, commonly assessed as the distance betwee...

PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks.

Journal of chemical information and modeling
This study introduces PROFIS, a new generative model capable of the design of structurally novel and target-focused compound libraries. The model relies on a recurrent neural network that was trained to decode embedded molecular fingerprints into SMI...