vNN Web Server for ADMET Predictions

• Main Authors: Patric Schyman, Ruifeng Liu, Valmik Desai, Anders Wallqvist
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2017-12-01
• Series: Frontiers in Pharmacology
• Subjects: ADME; toxicology; QSAR; machine learning; applicability domain; online web platform
• Online Access: http://journal.frontiersin.org/article/10.3389/fphar.2017.00889/full

In drug development, early assessments of pharmacokinetic and toxic properties are important stepping stones to avoid costly and unnecessary failures. Considerable progress has recently been made in the development of computer-based (in silico) models to estimate such properties. Nonetheless, such models can be further improved in terms of their ability to make predictions more rapidly, easily, and with greater reliability. To address this issue, we have used our vNN method to develop 15 absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction models. These models quickly assess some of the most important properties of potential drug candidates, including their cytotoxicity, mutagenicity, cardiotoxicity, drug-drug interactions, microsomal stability, and likelihood of causing drug-induced liver injury. Here we summarize the ability of each of these models to predict such properties and discuss their overall performance. All of these ADMET models are publically available on our website (https://vnnadmet.bhsai.org/), which also offers the capability of using the vNN method to customize and build new models.