Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials.

Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wave function. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term topogivity, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput procedure for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation.

Energetic Descriptors of Steric Hindrance in Real Space: An Improved IQA Picture*.

Steric hindrance (SH) plays a central role in the modern chemical narrative, lying at the core of chemical intuition. As it however happens with many successful chemical concepts, SH lacks an underlying physically sound root, and multiple mutually inconsistent approximations have been devised to relate this fuzzy concept to computationally derivable descriptors. We here argue that being SH related to spatial as well as energetic features of interacting systems, SH can be properly handled if we chose a real space energetic stance like the Interacting Quantum Atoms (IQA) approach. Drawing on previous work by Popelier and coworkers (ChemistryOpen 8, 560, 2019) we build an energetic estimator of SH, referred to as EST . We show that the rise in the self-energy of a fragment that accompanies steric congestion is a faithful proxy for the chemist's SH concept if we remove the effect of charge transfer. This can be done rigorously, and the EST here defined provides correct sterics even for hydrogen atoms, where the plain use of deformation energies leads to non-chemical results. The applicability of EST is validated in several chemical scenarios, going from atomic compressions to archetypal SN2 reactions. EST is shown to be a robust steric hindrance descriptor.

Chemical Intuition in Drug Design and Discovery.

The medicinal chemist plays the most important role in drug design, discovery and development. The primary goal is to discover leads and optimize them to develop clinically useful drug candidates. This process requires the medicinal chemist to deal with large sets of data containing chemical descriptors, pharmacological data, pharmacokinetics parameters, and in silico predictions. The modern medicinal chemist has a large number of tools and technologies to aid him in creating strategies and supporting decision-making. Alongside with these tools, human cognition, experience and creativity are fundamental to drug research and are important for the chemical intuition of medicinal chemists. Therefore, fine-tuning of data processing and in-house experience are essential to reach clinical trials. In this article, we will provide an expert opinion on how chemical intuition contributes to the discovery of drugs, discuss where it is involved in the modern drug discovery process, and demonstrate how multidisciplinary teams can create the optimal environment for drug design, discovery, and development.