SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions.

Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.

Bayesian Algorithm for Retrosynthesis.

The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion of the entire reaction space. At present, emerging machine learning technologies are reformulating the process of retrosynthetic planning. This study aimed to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of the training of a deep neural network, which is used to forwardly predict a product of the given reactants with a high level of accuracy, followed by inversion of the forward model into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. With a forward model prediction accuracy of approximately 87%, the Bayesian retrosynthesis algorithm successfully rediscovered 81.8 and 33.3% of known synthetic routes of one-step and two-step reactions, respectively, with top-10 accuracy. Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We also investigated the potential applicability of such diverse candidates based on expert knowledge of synthetic organic chemistry.

Formation mechanism for hexagonal-structured self-assemblies of nanocrystalline titania templated by cetyltrimethylammonium bromide.

Hexagonal-structured self-assemblies of nanocrystalline (anatase) titania templated by cetyltrimethylammonium bromide (C(16)H(33)N(CH(3))(3)Br; CTAB) (Hex-ncTiO(2)/CTAB Nanoskeleton) were formed after mixing of aqueous solutions containing CTAB spherical micelles and titanium oxysulfate acid hydrate (TiOSO(4).xH(2)SO(4).xH(2)O) as a titania precursor in the absence of any other additives. Formation mechanism of the Hex-ncTiO(2)/CTAB Nanoskeleton was examined in terms of the reaction temperature, titania precursor/CTAB mixing ratio, surfactant type, electrostatic interaction, micelle formation and molecular component. We found that crystal growth of crystalline (anatase) titania (polymorphic crystallization) was promoted with higher temperature and lower titania precursor content in aqueous solutions. In addition, we revealed that the crystalline (anatase) titania was formed in polycation, poly(allylamine hydrochloride ([CH(2)CH(CH(2)NH(2))HCl](n); PAH), and formamide (HCONH(2)) solutions. On the other hand, no titania formation was observed in anionic systems such as sodium dodecyl sulfate (CH(3)(CH(2))(11)OSO(3)Na; SDS) and poly(sodium 4-styrenesulfonate ([C(8)H(7)SO(3)Na](n); PSSS). This indicates that hydrolysis reaction of the titania precursor is initiated by not only cations but also nitrogen atoms in molecules and polymers. Hexagonally structure was formed in only cationic surfactant micellar solutions but not in polycation solutions and formamide.