How far have decision tree models come for data mining in drug discovery?

Machine learning (ML) methods assist in drug discovery mostly by way of data mining in virtual screening (VS). If the target is sufficiently characterized, say by knowledge of its threedimensional (3D) structure or gene sequence, we can take a structure-based VS (SBVS) approach and run molecular docking and dynamics, or 3D-similarity matching experiments. More often, however, we only know a set of molecular structures and their biological activities, and so we may perform a ligand-based VS (LBVS) [1]. The results of an LBVS, which are computationally much less expensive to obtain than those of an SBVS, can be the basis of chemical database queries and, optimally, at an early stage of drug discovery enhance our understanding of how a molecule’s action may come about (hypothesis generation). The concept of decision trees is well suited for this.

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