Journal ArticleDOI
Machine-learning approaches in drug discovery: methods and applications.
TLDR
This work focuses on machine-learning techniques within the context of ligand-based VS (LBVS), providing a detailed view of the current state of the art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances.About:
This article is published in Drug Discovery Today.The article was published on 2015-03-01. It has received 542 citations till now. The article focuses on the topics: Application domain.read more
Citations
More filters
Journal ArticleDOI
Automated exploration of the low-energy chemical space with fast quantum chemical methods
TL;DR: An efficient scheme for the in silico sampling for parts of the molecular chemical space by semiempirical tight-binding methods combined with a meta-dynamics driven search algorithm is proposed and discussed, opening many possible applications in modern computational chemistry and drug discovery.
Journal ArticleDOI
Molecular Docking: Shifting Paradigms in Drug Discovery.
Luca Pinzi,Giulio Rastelli +1 more
TL;DR: This review describes how molecular docking was firstly applied to assist in drug discovery tasks, and illustrates newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling.
Journal ArticleDOI
Deep Learning in Drug Discovery.
TL;DR: 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.
Journal ArticleDOI
Predicting reaction performance in C–N cross-coupling using machine learning
TL;DR: It is demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation and provides significantly improved predictive performance over linear regression analysis.
Journal ArticleDOI
From machine learning to deep learning: progress in machine intelligence for rational drug discovery.
TL;DR: The history of machine learning is summarized and insight into recently developed deep learning approaches and their applications in rational drug discovery are provided to help guide early-stage drug design and discovery in the current big data era.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.