Novel Reagent Space: Identifying Unorderable but Readily Synthesizable Building Blocks

Abstract

Drug discovery building blocks available commercially or within an internal inventory cover a diverse range of chemical space and yet describe only a tiny fraction of all chemically feasible reagents. Vendors will eagerly provide tools to search the former; there is no straightforward method of mining the latter. We describe a procedure and use case in assembling chemical structures not available for purchase but that could likely be synthesized in one robust chemical transformation starting from readily available building blocks. Accessing this vast virtual chemical space dramatically increases our curated collection of reagents available for medicinal chemistry exploration and novel hit generation, almost tripling the number of those with 10 or fewer atoms.

Interpretable Machine Learning Model for Predicting Interaction Energies between Dimethyl Sulfide and Potential Absorbing Solvents

“…To overcome these challenges, studies have focused on high-throughput screening and performance prediction of enormous amounts of molecules in the chemical space by means of artificial intelligence methods. 13 Driven by both the expanded chemical database and the advanced algorithms, machine learning (ML) has been finding powerful functions and wide applications in designing molecules and infrastructure for broad engineering areas, including chemistry, 14 material, 15 biology, 16 medicine, 17,18 environment, 19 and electronics. 20,21 ML algorithms have been used to aid solvent discovery by predicting the solubilities of various species, 22 diffusion coefficients, 23 and reaction paths.…”

“…Driven by both the expanded chemical database and the advanced algorithms, machine learning (ML) has been finding powerful functions and wide applications in designing molecules and infrastructure for broad engineering areas, including chemistry, material, biology, medicine, , environment, and electronics. , ML algorithms have been used to aid solvent discovery by predicting the solubilities of various species, diffusion coefficients, and reaction paths . Quantitative structure–activity relationship (QSAR) models were explored using extensive training data sets and descriptors. , Using sufficient solubility data, Orlov et al and Shi et al have successfully used ML methods to achieve solubility prediction and solvent identification for the absorption of H 2 S and CO 2 , respectively.…”

Unveiling the Quantitative Relationships between Electron Distribution and Steric Hindrance of Organic Amines and Their Reaction Rates with Carbonyl Sulfur: A Theoretical Calculation Investigation

“…The computer-aided strategies, such as artificial intelligence (AI) technologies for high-throughput screening and big data-based models for reaction path prediction, have been employed to facilitate solvent exploitation. − However, the current AI algorithms and related models are mostly based on the black-box strategies . As a result, only a collection of candidate solvents can be screened by means of the black-box models; the precisely targeted molecules are reached via different scales of solvent evaluating experiments.…”

α-Metalated Isocyanides Toward a Tangible Reagent Space

“…16 Commercially unavailable but tangible compound libraries are of the utmost importance not only for medicinal chemists, but also for others. 17 Herein, we demonstrate that αmetalated isocyanides serve as an important synthetic tool for the acquisition of ready-to-screen libraries, as exemplified by the synthesis of oxazole derivatives. The choice of this specific heterocycle 18 was made because of its privileged character due to its abundance in both bioactive natural products [19][20][21] and commercially available drugs.…”