This article provides an introduction to and review of the field of computer-aided molecular design (CAMD). It is intended to be approachable for the absolute beginner as well as useful to the seasoned CAMD practitioner. We begin by discussing various quantitative structure-property relationships (QSPRs) which have been demonstrated to work well with CAMD problems. The methods discussed in this article are (1) group contribution methods, (2) topological indices, and (3) signature descriptors. Next, we present general optimization formulations for various forms of the CAMD problem. Common design constraints are discussed and structural feasibility constraints are provided for the three types of QSPRs addressed. We then detail useful techniques for approaching CAMD optimization problems, including decomposition methods, heuristic approaches, and mathematical programming strategies. Finally, we discuss many applications that have been addressed using CAMD.

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In this article, we investigate reaction solvent design using COSMO-RS thermodynamics in conjunction with computeraided molecular design (CAMD) techniques. CAMD using COSMO-RS has the distinct advantage of being a method based in quantum chemistry, which allows for the incorporation of quantum-level information about transition states, reactive intermediates, and other important species directly into CAMD problems. This work encompasses three main additions to our previous framework for solvent design (Austin et al., Chem Eng Sci. 2017;159:93-105): (1) altering the group contribution method to estimate hydrogen-bonding and non-hydrogen-bonding r-profiles; (2) ab initio modeling of strong solute/solvent interactions such as H-bonding or coordinate bonding; and (3) solving mixture design problems limited to common laboratory and industrial solvents. We apply this methodology to three diverse case studies: accelerating the reaction rate of a Menschutkin reaction, controlling the chemoselectivity of a lithiation reaction, and controlling the chemoselectivity of a nucleophilic aromatic substitution reaction. We report improved solvents/mixtures in all cases.

In this work, we propose a new methodology for mixture design. By projecting the problem on the space of individual component properties, the methodology exploits a natural problem decomposition and capitalizes on fast methods for pure compound design and mixture fraction design. We demonstrate the proposed methodology through application to two illustrative examples and then to two problems from the mixture design literature concerning the purification of ibuprofen. In all cases, the proposed approach finds optimal solutions while exploring a small number of feasible molecular structures. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1514–1530, 2016

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