Journal of chemical information and modeling
Jan 24, 2019
Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excr...
In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, includin...
Journal of chemical information and modeling
Jan 11, 2019
Deep learning has drawn significant attention in different areas including drug discovery. It has been proposed that it could outperform other machine learning algorithms, especially with big data sets. In the field of pharmaceutical industry, machin...
Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews
Jan 10, 2019
In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have bee...
Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews
Jan 10, 2019
We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox mode...
Journal of biomolecular structure & dynamics
Dec 24, 2018
Matrix metal proteinases-12 (MMP-12) is a hot pharmaceutical target on the treatment of many human diseases. There's a crying need for designing and finding new MMP-12 inhibitors. In this work, four machine learning approaches, support vector machine...
Journal of chemical information and modeling
Dec 14, 2018
Support vector regression (SVR) is a premier approach for the prediction of compound potency. Given the conceptual link between support vector machine (SVM) and SVR modeling, SVR is capable of accounting for continuous and discontinuous structure-act...
The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the mos...
Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learni...
We present two approaches for the computation of hydrogen bond acceptor strengths, one by machine-learning and one by a composite quantum-mechanical protocol, both based on the well-established pK scale and dataset. The QM calculations after a necess...