Combinatorial Chemistry & High Throughput Screening - Volume 12, Issue 4, 2009

Computer-assisted drug design is used to increase the chances of finding valuable drug candidates, by applying a wide range of computational methods, such as machine learning, structure-activity relationships, quantitative structure-activity relationships, molecular mechanics, quantum mechanics, molecular dynamics, and drug-protein docking. Machine learning is an important field of artificial intelligence, and includes a Read More

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support v Read More

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compound Read More

Computational screening of compound databases has become increasingly popular in pharmaceutical research. This review focuses on the evaluation of ligand-based virtual screening using active compounds as templates in the context of drug discovery. Ligand-based screening techniques are based on comparative molecular similarity analysis of compounds with known and unknown activity. We provide an over Read More

Cytochromes P450 (CYPs) are crucial targets when predicting the ADME properties (absorption, distribution, metabolism, and excretion) of drugs in development. Particularly, CYPs mediated drug-drug interactions are responsible for major failures in the drug design process. Accurate and robust screening filters are thus needed to predict interactions of potent compounds with CYPs as early as possible in the process. In recent ye Read More

The identification of new lead structures is a pivotal task in early drug discovery. Molecular de novo design of ligand structures has been successfully applied in various drug discovery projects. Still, the question of the scaffold hopping potential of drug design by adaptive evolutionary optimization has been left unanswered. It was unclear whether de novo design is actually able to leap away from given chemotypes (“ac Read More

The initial stage of drug development is the hit (active) compound search from a pool of millions of compounds; for this process, in silico (virtual) screening has been successfully applied. One of the problems of in silico screening, however, is the low hit ratio in relation to the high computational cost and the long CPU time. This problem becomes serious in structure-based in silico screening. The major reason is the Read More

Support vector machines and kernel methods have recently gained considerable attention in chemoinformatics. They offer generally good performance for problems of supervised classification or regression, and provide a flexible and computationally efficient framework to include relevant information and prior knowledge about the data and problems to be handled. In particular, with kernel methods molecules do n Read More

A mutual information based activity labeling and scoring (MIBALS) approach to reverse fingerprint analysis is presented. Whole molecule scores produced by the method are shown to be capable of ranking compounds in virtual highthroughput screening (vHTS) experiments, while fragment scores produced by the method are able to identify pharmacophore moieties important for biological activity. The performance of MIBA Read More

Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e.g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i. Read More