AI Medical Compendium Journal:
Molecular informatics

Showing 101 to 110 of 113 articles

Determination of Meta-Parameters for Support Vector Machine Linear Combinations.

Molecular informatics
Support vector machines (SVMs) are among the most popular machine learning methods for compound classification and other chemoinformatics tasks such as, for example, the prediction of ligand-target pairs or compound activity profiles. Depending on th...

Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.

Molecular informatics
There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on h...

GTM-Based QSAR Models and Their Applicability Domains.

Molecular informatics
In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the l...

A Machine Learning Approach to Explain Drug Selectivity to Soluble and Membrane Protein Targets.

Molecular informatics
Improved understanding of the forces that determine drug specificity to their targets is important for drug design and discovery, as well as for gaining knowledge about molecular recognition. Here, we present a machine learning approach that includes...

PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation.

Molecular informatics
Identification of DNA-binding proteins is an important problem in biomedical research as DNA-binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the-state-of-the-art performance with different ...

Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.

Molecular informatics
The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of certain molecules between the general somatic circulatory system to the central nervous system (CNS). While the BBB maintains homeostasis by regulating ...

Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values.

Molecular informatics
Feature attribution methods from explainable artificial intelligence (XAI) provide explanations of machine learning models by quantifying feature importance for predictions of test instances. While features determining individual predictions have fre...

An Integrated Fuzzy Neural Network and Topological Data Analysis for Molecular Graph Representation Learning and Property Forecasting.

Molecular informatics
Within a recent decade, graph neural network (GNN) has emerged as a powerful neural architecture for various graph-structured data modelling and task-driven representation learning problems. Recent studies have highlighted the remarkable capabilities...

Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.

Molecular informatics
The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly breast cancer. Discovering novel HER2 inhibitors is crucial for expanding therapeutic options for HER...

Prediction of the Appropriate Temperature and Pressure for Polymer Dissolution Using Machine Learning Models.

Molecular informatics
The widespread use of polymer solutions in the chemical industry poses a significant challenge in determining optimal dissolution conditions. Traditionally, researchers have relied on experimental methods to estimate the processing parameters needed ...