MEDICASCY is a multi-label based boosted random forest machine learning method that predicts small molecule’s side effects, indications, efficacy and mode of action proteins. MEDICASCY server takes a small molecule’s SMILES string (example: CC(C)(c1ccc(O)cc1)c2ccc(Cl)cc2 ) as input and predicts the molecule’s side effects and indications and sends output via user provided email to the user.
Notice: This server is freely available to all academic and non-commercial users.
Commercial users – to use this server, or request an evaluation copy, please send an email to Dr. Jeffrey Skolnick: firstname.lastname@example.org.
Citation: Zhou, H, Cao H, Matyunina L, Shelby M, Cassels L, McDonald J, Skolnick, J. In Press. MEDICASCY: A Machine Learning Approach for Predicting Small Molecule Drug Side Effects, Indications, Efficacy and Mode of Action. Molecular Pharmaceutics doi.org/10.1021/acs.molpharmaceut.9b01248. PDF
Please send questions to Dr. Hongyi Zhou: email@example.com.