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Ligands

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Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening.

Journal of chemical information and modeling
In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein-ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a standardized pocket Pfam-based clustering (Pfam-cluster) appr...

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning.

Journal of medicinal chemistry
Unexpected metabolism in modification and conjugation phases can lead to the failure of many late-stage drug candidates or even withdrawal of approved drugs. Thus, it is critical to predict the sites of metabolism (SoM) for enzymes, which interact wi...

Premexotac: Machine learning bitterants predictor for advancing pharmaceutical development.

International journal of pharmaceutics
Bitter taste receptors were recently found to be involved in numerous physiological and pathological conditions other than taste and are suggested as potential drug targets. In vivo and in vitro techniques for screening bitterants as ligands come wit...

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods.

Journal of chemical information and modeling
We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for red...

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features.

Journal of chemical information and modeling
Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estim...

EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive pe...

Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures.

Journal of chemical information and modeling
The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein str...

Effects of data quality and quantity on deep learning for protein-ligand binding affinity prediction.

Bioorganic & medicinal chemistry
Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A number of deep learning approaches have been developed in recent years to improve the accuracy of such affinity prediction. While the predicting power of t...

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules.

Journal of chemical information and modeling
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to...

Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design.

Journal of chemical information and modeling
In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Diff...