Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Medicine, Research & Experimental, Pharmacology & Pharmacy, Research & Experimental Medicine, computer aided drug design, in silico modeling, crystal structure, solubility, hydrogen bonding, machine learning, inductive logic programming, PERMEABILITY, DESCRIPTORS, PATTERNS, DESIGN, Artificial Intelligence, Computer Simulation, Crystallization, Decision Trees, Drug Design, Electronic Data Processing, Forecasting, Hydrogen Bonding, Models, Biological, Molecular Conformation, Molecular Structure, Software, 0303 Macromolecular and Materials Chemistry, 1115 Pharmacology and Pharmaceutical Sciences, 3214 Pharmacology and pharmaceutical sciences

Abstract:

In the domain of crystal engineering, various schemes have been proposed for the classification of hydrogen bonding (H-bonding) patterns observed in 3D crystal structures. In this study, the aim is to complement these schemes with rules that predict H-bonding in crystals from 2D structural information only. Modern computational power and the advances in inductive logic programming (ILP) can now provide computational chemistry with the opportunity for extracting structure-specific rules from large databases that can be incorporated into expert systems. ILP technology is here applied to H-bonding in crystals to develop a self-extracting expert system utilizing data in the Cambridge Structural Database of small molecule crystal structures. A clear increase in performance was observed when the ILP system DMax was allowed to refer to the local structural environment of the possible H-bond donor/acceptor pairs. This ability distinguishes ILP from more traditional approaches that build rules on the basis of global molecular properties.