Assists users for bitter-sweet taste prediction. BitterSweet is an integrative framework with state-of-the-art machine learning models based on chemical descriptors. Its models were able to predict the sweet taste, along with probability values, of all molecules in the test except for a small fraction for which molecular descriptors could not be generated due to incomplete structures.
Aims to optimize drug discovery process. BIOVIA Discovery Studio is a comprehensive suite of science applications. It offers a visualization and collaboration framework that includes a comprehensive science portfolio. It provides several tools for simulations, for macromolecule design and analysis, for antibody development, for structure-based design (SBD), for fragment-based design (FBD) or for pharmacophore and ligand-based design.
Predicts many kinds of biological activity for compounds from different chemical series based on their 2D structural formulas. PASS finds new targets mechanisms for some ligands. It can reveal new ligands for some biological targets. The tool can be used to analyze the occurrence, in a database, of compounds predicted to be active for a well-defined set of PASS activities.
An easy-to-use, readily interpretable algorithm and tool that can assist scientists in navigating a complex scientific and informational landscape. In particular, Badapple is designed for rapid detection of promiscuity patterns in HTS data, using public bioassay evidence. However, Badapple is designed to be trained with additional data, and to detect novel patterns, based on an entirely different chemical library. Compound promiscuity is generally undesirable but must be understood in light of polypharmacology and systems chemical biology. Badapple scores indicate either patterns of true or artefactual promiscuity, either of which can help guide an experimental research project away from “false trails”.
Allows users to perform electrophysiology simulations. ActionPotential is an open source portal, also available as a standalone software, which intends to evaluate the performance of different models and to define suitable contexts of use. It has two main functions: (i) gathers data from a cardiac ion channel screening panel and define expectations of the likely total effect, in multiple situations (ii) determines QT liability of compounds and possibly design new experiments.
An efficient graph-based algorithm to predict peptides with the highest biological activity for machine learning predictors using the GS kernel. Combined with a multi-target model, it can be used to predict binding motifs for targets with no known ligands.
Allows users to mine data for quantitative structure–activity relationship and quantitative structure–property relationships (QSAR/QSPR) studies. Autoweka is a standalone software based on support vector machines and artificial neural networks with the aim of facilitating QSAR/QSPR predictive data mining models’ development. Besides, users can download additionally scripts to generate plots and graphs.