Journal Article10.1021/CI0202741
A Consensus Neural Network-Based Technique for Discriminating Soluble and Poorly Soluble Compounds
David T. Manallack,Benjamin G Tehan,Emanuela Gancia,Brian D Hudson,Martyn G Ford,David J. Livingstone,David C. Whitley,William R. Pitt +7 more
62
TL;DR: This paper presents studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonablesolubility, intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.
read more
Abstract: BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
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.
691
Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4.
TL;DR: Characterizing both the ability of a virtual screening workflow to select active molecules and the ability to discard inactive ones, the ROC curve approach is well suited for this critical decision gate.
613
Can we estimate the accuracy of ADME-Tox predictions?
TL;DR: An analysis of octanol-water distribution coefficients is used to exemplify the consistency of estimated and calculated accuracy of the ALOGPS program (http://www.vcclab.org) to predict in-house and publicly available datasets.
253
Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Artem Cherkasov,Jiazhong Li,Paola Gramatica,Katja Hansen,Timon Schroeter,Klaus-Robert Müller,Lili Xi,Huanxiang Liu,Xiaojun Yao,Tomas Öberg,Farhad Hormozdiari,Phuong Dao,Cenk Sahinalp,Roberto Todeschini,Pavel G. Polishchuk,A. Artemenko,Victor E. Kuz’min,Todd M. Martin,Douglas M. Young,Denis Fourches,Eugene N. Muratov,Alexander Tropsha,Igor I. Baskin,Dragos Horvath,Gilles Marcou,Christophe Muller,A. Varnek,Volodymyr V. Prokopenko,Igor V. Tetko +32 more
TL;DR: This work demonstrates that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs and can be used to halve the cost of experimental measurements by providing a similar prediction accuracy.
242
Prediction of ADMET Properties
TL;DR: Some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties are described and the reader is made aware of some of the challenges involved in deriving useful in-silico ADMET models for drug development.
239
References
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
14.5K
Neural Networks for Pattern Recognition
Christopher M. Bishop
- 23 Nov 1995
Abstract: Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
9.8K
Molecular properties that influence the oral bioavailability of drug candidates.
Daniel F. Veber,Stephen R. Johnson,Hung-Yuan Cheng,Brian R. Smith,Keith W. Ward,Kenneth D. Kopple +5 more
TL;DR: Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count are found to be important predictors of good oral bioavailability, independent of molecular weight.
7K
A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases.
TL;DR: The effective range of physicochemical properties presented here can be used in the design of drug-like combinatorial libraries as well as in developing a more efficient corporate medicinal chemistry library.
2.5K
•Book
Netlab: Algorithms for Pattern Recognition
Ian T. Nabney
- 01 Jan 2002
TL;DR: This chapter discusses Parameter Optimisation Algorithms, Density Modelling and Clustering, Single-Layer Networks, and Radial Basis Functions.
1.1K
Related Papers (5)
Tatiana I. Netzeva,Andrew Worth,Tom Aldenberg,Romualdo Benigni,Mark T. D. Cronin,Paola Gramatica,Joanna Jaworska,Scott Kahn,Gilles Klopman,Carol A. Marchant,Glenn J. Myatt,Nina Nikolova-Jeliazkova,Grace Patlewicz,Roger Perkins,David W. Roberts,Terry W Schultz,David T. Stanton,Johannes J.M. van de Sandt,Weida Tong,Gilman Veith,Chihae Yang +20 more
[...]
[...]
Leo Breiman
- 01 Oct 2001