Neural Networks in Building QSAR Models

  title={Neural Networks in Building QSAR Models},
  author={Igor I. Baskin and Vladimir A. Palyulin and Nikolai S. Zefirov},
  journal={Methods in molecular biology},
This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resulting models, the issues of generalization and memorization, the problems of overfitting and… 

3D-QSAR in drug design--a review.

This review seeks to provide a bird's eye view of the different 3D-QSAR approaches employed within the current drug discovery community to construct predictive structure-activity relationships and discusses the limitations that are fundamental to these approaches, as well as those that might be overcome with the improved strategies.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

The old pitfalls of overtraining and interpretability are still present with ANNs, but the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR.

Development of quantitative structure-metabolism (QSMR) relationships for substituted anilines based on computational chemistry

Calculation of physicochemical properties incorporating the effect of solvation using ab initio methods improved the classification model in terms of both the visual separation in multivariate projections and prediction accuracy.

A renaissance of neural networks in drug discovery

This review discusses traditional and newly emerging neural network approaches to drug discovery, focusing on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning.

Molecular design and QSARs/QSPRs with molecular descriptors family.

The MDF methodology developed and implemented as a platform for investigating and characterizing quantitative relationships between the chemical structure and the activity/property of active compounds was used on more than 50 study cases and the methodology allowed obtaining of QSAR/QSPR models improved in explanatory power of structure-activity and structure-property relationships.

QSAR studies of the dispersion of SWNTs in different organic solvents

Artificial neural network (ANN) and multiple linear regression (MLR) approaches were successfully applied to construct quantitative structure–activity relationship models of the dispersibility of

Consensus Drug Design Using IT Microcosm

The adequacy, validity and high accuracy of IT Microcosm are demonstrated via sample predictions of the various pharmacological activities of structurally similar and structurally diverse organic compounds, complex organic salts, supramolecular complexes and substance mixtures, accounting for the synergy between the individual components of mixtures.

Using artificial neural networks to predict cell-penetrating compounds

The article looks at three main systems including the BBB, gastrointestinal absorption and permeation in addition to discussing a new approach for cell-penetrating peptide discovery.



QSAR/QSPR Studies Using Probabilistic Neural Networks and Generalized Regression Neural Networks

The Probabilistic Neural Network (PNN) and its close relative, the Generalized Regression Neural Network (GRNN), are presented as simple yet powerful neural network techniques for use in Quantitative

Volume learning algorithm artificial neural networks for 3D QSAR studies.

The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm, indicating a new convenient tool for three-dimensional QSAR studies.

Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines

An application of recursive cascade correlation (CC) neural networks to quantitative structure-activity relationship (QSAR) studies is presented, with emphasis on the study of the internal

On the Physical Interpretation of QSAR Models

A method of model interpretation that employs partial least squares (PLS) analysis is illustrated using QSAR equations developed for the inhibition of quinolone-resistant bacterial DNA gyrase and human topoisomerase-II inhibition by a series of quino antibacterial agents.

Review Biologicals & Immunologicals: Applications of artificial neural networks to quantitative structure-activity relationships

New applications of ANNs, including methods of descriptor optimisation, simultaneous prediction of multiple descriptors, the development of flexible pharmacophore models and new methods for the multidimensional reduction and display of data sets suggest that ANNs will have a useful role in the field of QSAR in the future.

An approach to the interpretation of backpropagation neural network models in QSAR studies

An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is

Neural Network Studies, 2. Variable Selection

Five pruning algorithms designed to estimate the importance of input variables in feed-forward artificial neural network trained by back propagation algorithm (ANN) applications and to prune nonrelevant ones in a statistically reliable way are introduced and investigated.

On the Use of Neural Network Ensembles in QSAR and QSPR

It is demonstrated that bagging may not be the best possible choice and that simpler techniques such as retraining with the full sample can often produce superior results, which are rationalized using Krogh and Vedelsby's decomposition of the generalization error.

Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks.

A new hybrid method (GNN) combining a genetic algorithm and an artificial neural network has been developed for quantitative structure-activity relationship (QSAR) studies, and it is essential to have one each for the steric, electrostatic, and hydrophobic attributes of a drug candidate to obtain a satisfactory QSAR for this data set.