Connectivity stepwise derivation (CSD) method: a generic chemical structure information extraction method for the full step matrix

Abstract

Emerging advanced exploration modalities such as property prediction, molecular recognition, and molecular design boost the fields of chemistry, drugs, and materials. Foremost in performing these advanced exploration tasks is how to describe/encode the molecular structure to the computer, i.e., from what the human eye sees to what is machine-readable. In this effort, a chemical structure information extraction method termed connectivity step derivation (CSD) for generating the full step matrix (MS_F) is exhaustively depicted. The CSD method consists of structure information extraction, atomic connectivity relationship extraction, adjacency matrix generation, and MS_F generation. For testing the run speed of the MS_F generation, over 54 000 molecules have been collected covering organic molecules, polymers, and MOF structures. Test outcomes show that as the number of atoms in a molecule increases from 100 to 1000, the CSD method has an increasing advantage over the classical Floyd–Warshall algorithm, with the running speed rising from 28.34 to 289.95 times in the Python environment and from 2.86 to 25.49 times in the C++ environment. The proposed CSD method, that is, the elaboration of chemical structure information extraction, promises to bring new inspiration to data scientists in chemistry, drugs, and materials as well as facilitating the development of property modeling and molecular generation methods.