Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks.

  title={Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks.},
  author={Fahimeh Ghasemi and Alireza Mehridehnavi and Alfonso P{\'e}rez-Garrido and Horacio P{\'e}rez‐S{\'a}nchez},
  journal={Drug discovery today},
  volume={23 10},

The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools

Deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand‐based virtual screening, but further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery.

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics.

The results indicate the potential benefits of employing the DL strategies in the drug discovery process and investigate the performance of several algorithms, including deep neural networks, convolutional neural networks and multi-task learning, with the aim of generating high-quality, interpretable big and diverse databases for drug design and development.

CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling

The convolutional recurrent neural network and transfer learning (CRNNTL) method inspired by the applications of polyphonic sound detection and electrocardiogram classification is proposed and efficient knowledge transfer is achieved to overcome data scarcity considering binding site similarity between different targets.

The power of deep learning to ligand-based novel drug discovery

  • I. Baskin
  • Computer Science
    Expert opinion on drug discovery
  • 2020
Several architectures of neural networks for building either discriminative or generative models are considered in this paper, including deep multilayer neural networks, different kinds of convolutional Neural networks, recurrent neural Networks, and several types of autoencoders.

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

It is believed that the continuous improvement in ML‐based SFs can surely guide the early‐stage drug design and accelerate the discovery of new drugs.

Discovery of CDK4 inhibitors by convolutional neural networks.

Depending only on intuitive information, the developed method was shown to be feasible, thus providing a new method of lead compound discovery.

Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks

It is possible to build QSAR models using LSTMs without using pre-computed traditional descriptors, and models are far from being “black box,” according to this study.

Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions

This chapter provides a comprehensive investigation of fundamentals, state-of-the-art and some perspectives to speed up the process of the design, optimization and production of the medicine for COVID-19 based on Deep Learning (DL) methods.

A Convolutional Neural Network for Virtual Screening of Molecular Fingerprints

A Virtual Screening procedure based on Convolutional Neural Networks is presented, that is aimed at classifying a set of candidate compounds as regards their biological activity on a particular target protein.



Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

A brief overview of deep learning methods is presented and in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties, by considering an ensemble of recursive neural networks associated with all possible vertex-centered acyclic orientations of the molecular graph.

Multi-task Neural Networks for QSAR Predictions

This work used an artificial neural network to learn a function that predicts activities of compounds for multiple assays at the same time and compared its methods to alternative methods reported to perform well on these tasks and found that the neural net methods provided superior performance.

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized and hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons.

Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships

It is shown that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort.

The role of different sampling methods in improving biological activity prediction using deep belief network

The impact of DBN was applied based on the different sampling approaches mentioned above to initialize the DNN architecture in predicting biological activity of all fifteen Kaggle targets that contain more than 70k molecules.

Deep Learning in Drug Discovery

An overview of this emerging field of molecular informatics, the basic concepts of prominent deep learning methods are presented, and motivation to explore these techniques for their usefulness in computer‐assisted drug discovery and design is offered.

Neural networks as data mining tools in drug design

Neural networks are powerful data mining tools with a wide range of applications in drug design. This paper largely concentrates on self-organizing neural networks that can be used for investigating

Boosting compound-protein interaction prediction by deep learning

This study proposes a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs.

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR

The differences in approach between deep and shallow neural networks are described, their abilities to predict the properties of test sets for 15 large drug data sets are compared, the results in terms of the Universal Approximation theorem are discussed, and how DNN may ameliorate or remove troublesome “activity cliffs” in QSAR data sets.