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Quantitative Structure-Activity Relationship

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Quantitative structure-activity relationship analysis using deep learning based on a novel molecular image input technique.

Bioorganic & medicinal chemistry letters
Quantitative structure-activity relationship (QSAR) analysis uses structural, quantum chemical, and physicochemical features calculated from molecular geometry as explanatory variables predicting physiological activity. Recently, deep learning based ...

Prototype-Based Compound Discovery Using Deep Generative Models.

Molecular pharmaceutics
Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large ( Polishchuk , P. G. ; Madzhidov , T. I. ; Varnek , A. Estimation of the size of drug-like chemical space based on GDB-17 data . J. Comput.-Aid...

Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme.

Chemical research in toxicology
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacop...

Deep reinforcement learning for de novo drug design.

Science advances
We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ...

Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme.

Molecules (Basel, Switzerland)
P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood⁻brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorp...

PV Analyzer: A Decision Support System for Photovoltaic Solar Cells Libraries.

Molecular informatics
This work describes the integration of several data mining and machine learning tools for researching Photovoltaic (PV) solar cells libraries into a unified workflow embedded within a GUI-supported Decision Support System (DSS), named PV Analyzer. Th...

Classification of thyroid hormone receptor agonists and antagonists using statistical learning approaches.

Molecular diversity
In silico models are presented for modeling and predicting thyroid hormone receptor (TR) agonists and antagonists. A data set consisting of 258 compounds is used in the present work. The C4.5, random forest (RF) and support vector machine (SVM) stati...

Recent applications of machine learning in medicinal chemistry.

Bioorganic & medicinal chemistry letters
In recent decades, artificial intelligence and machine learning have played a significant role in increasing the efficiency of processes across a wide spectrum of industries. When it comes to the pharmaceutical and biotechnology sectors, numerous too...