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Quantitative Structure-Activity Relationship

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Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.

Methods in molecular biology (Clifton, N.J.)
Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to ...

Three-Dimensional Classification Structure-Activity Relationship Analysis Using Convolutional Neural Network.

Chemical & pharmaceutical bulletin
Quantitative structure-activity relationship (QSAR) techniques, especially those that possess three-dimensional attributes, such as the comparative molecular field analysis (CoMFA), are frequently used in modern-day drug design and other related rese...

Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique.

Anti-cancer agents in medicinal chemistry
Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal ...

Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity.

Toxicological sciences : an official journal of the Society of Toxicology
Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to ou...

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning.

Combinatorial chemistry & high throughput screening
BACKGROUND: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.

Intelligently Applying Artificial Intelligence in Chemoinformatics.

Current topics in medicinal chemistry
The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an i...

Predicting Inhibitors for Multidrug Resistance Associated Protein-2 Transporter by Machine Learning Approach.

Combinatorial chemistry & high throughput screening
BACKGROUND: The efflux transporter multidrug resistance associated protein-2 belongs to ATP-binding cassette superfamily which plays an important role in multidrug resistance and drugdrug interactions. Efflux transporters are considered to be importa...

Approaching Pharmacological Space: Events and Components.

Methods in molecular biology (Clifton, N.J.)
With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, ...