Reliable Target Prediction of Bioactive Molecules Based on Chemical Similarity Without Employing Statistical Methods - Forouzesh, Abed; Samadi Foroushani, Sadegh; Forouzesh, Fatemeh; Zand, Eskandar (Frontiers in Pharmacology, 2019)
The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by CTPTs. However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. The proposed method has several distinctive features compared to the available computational target prediction methods. First, the prediction is performed without employing statistical methods. Second, it is highly accurate. Third, it can be used appropriately without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Fourth, it enables us to gain a deeper understanding (more informative) of the relationship between the chemical structure and the target. Fifth, little knowledge regarding high-performance computing techniques or algorithms does not prevent its implementation. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred and SPiDER) which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB and PASS online showed the best quality and quantity for the accurate predictions.