Rapid Route-Finding for Bifurcating Organic Reactions.

A large number of organic reactions feature post-transition-state bifurcations. Selectivities in such reactions are difficult to analyze because they cannot be determined by comparing the energies of competing transition states. Molecular dynamics approaches can provide answers but are computationally very expensive. We present an algorithm that predicts the major products in bifurcating organic reactions with negligible computational cost. The method requires two transition states, two product geometries, and no additional information. The algorithm correctly predicts the major product for about 90% of the organic reactions investigated. For the remaining 10% of the reactions, the algorithm returns a warning indication that the conclusion may be uncertain. The method also reproduces the experimental and the molecular dynamics product ratios within 15% for more than 80% of the reactions. We have successfully applied the method to a trifurcating organic reaction, a carbocation rearrangement, and solvent-dependent Pummerer-like reactions, demonstrating the power of the algorithm to simplify and to help understand highly complex reactions.Trinity College, University of Cambridge Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1

Total Synthesis of Naturally Occurring 5,7,8-Trioxygenated Homoisoflavonoids

Homoisoflavonoids are in the subclass of the larger family of flavonoids but have one more alkyl carbon than flavonoids. Among them, 5,7,8-trioxygenated homoisoflavonoids have not been extensively studied for synthesis and biological evaluation. Our current objective is to synthesize 2 5,7,8-trioxygenated chroman-4-ones and 12 5,7,8-trioxygenated homoisoflavonoids that have been isolated from the plants Bellevalia eigii, Drimiopsis maculata, Ledebouria graminifolia, Eucomis autumnalis, Eucomis punctata, Eucomis pallidiflora, Chionodoxa luciliae, Muscari comosum, and Dracaena cochinchinensis. For this purpose, 1,3,4,5-tetramethoxybenzene and 4'-benzyloxy-2',3'-dimethoxy-6'-hydroxyacetophenone were used as starting materials. Asymmetric transfer hydrogenation using Noyori's Ru catalyst provided 5,7,8-trioxygenated-3-benzylchroman-4-ones with R-configuration in high yield and enantiomeric excess. By selective deprotection of homoisoflavonoids using BCl3, the total synthesis of natural products including 10 first syntheses and three asymmetric syntheses has been completed, and three isomers of the reported dracaeconolide B could be provided. Our research on 5,7,8-trioxygenated homoisoflavonoids would be useful for the synthesis of related natural products and pharmacological applications

Enantioselective Synthesis of Homoisoflavanones by Asymmetric Transfer Hydrogenation and Their Biological Evaluation for Antiangiogenic Activity

Neovascular eye diseases are a major cause of blindness. Excessive angiogenesis is a feature of several conditions, including wet age-related macular degeneration, proliferative diabetic retinopathy, and retinopathy of prematurity. Development of novel anti-angiogenic small molecules for the treatment of neovascular eye disease is essential to provide new therapeutic leads for these diseases. We have previously reported the therapeutic potential of anti-angiogenic homoisoflavanone derivatives with efficacy in retinal and choroidal neovascularization models, although these are racemic compounds due to the C3-stereogenic center in the molecules. This work presents asymmetric synthesis and structural determination of anti-angiogenic homoisoflavanones and pharmacological characterization of the stereoisomers. We describe an enantioselective synthesis of homoisoflavanones by virtue of ruthenium-catalyzed asymmetric transfer hydrogenation accompanying dynamic kinetic resolution, providing a basis for the further development of these compounds into novel experimental therapeutics for neovascular eye diseases

Small-molecule inhibitors of ferrochelatase are antiangiogenic agents

Activity of the heme synthesis enzyme ferrochelatase (FECH) is implicated in multiple diseases. In particular, it is a mediator of neovascularization in the eye and thus an appealing therapeutic target for preventing blindness. However, no drug-like direct FECH inhibitors are known. Here, we set out to identify small-molecule inhibitors of FECH as potential therapeutic leads using a high-throughput screening approach to identify potent inhibitors of FECH activity. A structure-activity relationship study of a class of triazolopyrimidinone hits yielded drug-like FECH inhibitors. These compounds inhibit FECH in cells, bind the active site in cocrystal structures, and are antiangiogenic in multiple in vitro assays. One of these promising compounds was antiangiogenic in vivo in a mouse model of choroidal neovascularization. This foundational work may be the basis for new therapeutic agents to combat not only ocular neovascularization but also other diseases characterized by FECH activity

Coverage Path Planning Fused Multi-UAV Source Term Estimation

Department of Mechanical EngineeringWhen a gas leak occurs from an industrial complex, the leakage point, or source term location must be determined. The presented algorithm suggests a strategy that allows multiple unmanned aerial vehicles (UAV) to estimate a source term location with high accuracy within a grid-based area. The strategy consists of three main algorithmic steps: cellular decomposition, optimal coverage path planning, and information theory-based movement policy. Suggested cellular decomposition method divides the area to designate a conflict-free sub-search-space to individual UAV, while accounting the assigned individual flight velocity, and take-off position. The path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the take-off position, and it recursively updates the nearest end point set to generate the shortest path. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest end point set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependentthe policy imposes the total path-length and turning number criteria per candidate. Vehicles send measurements and position data to a single particle filter for source term estimation while pursuing the coverage paths. As particle filter converges to a certain level, the vehicles are attracted toward arbitrary radius from the particle filter mean. UAVs compare the measurement data to confirm whether the actual source position is nearby the particle filter mean. The presented method demonstrates that UAVs estimate the source term location with high accuracy in numerical simulation and field experiment.ope

Computational Analysis of Molecules and Chemical Reactions from Cheminformatic Databases

A large number of organic reactions feature post-transition state bifurcations. Selectivities in such reactions are difficult to analyse because they cannot be determined by comparing the energies of competing transition states. Molecular dynamics approaches can provide answers but are computationally very expensive. In Chapter 1, a new ‘ValleyRidge’ algorithm has been developed which predicts the major products in bifurcating organic reactions with a negligible computational cost. The method requires two transition states, two product geometries and no additional information. The algorithm correctly predicts the major product for about 90% of the organic reactions investigated. For the remaining 10% of the reactions, the algorithm returns a warning sign, an indication that the conclusion may be uncertain. The method also reproduces the experimental or the molecular dynamics product ratios within the 15% error for more than 80% of the reactions. The method has been successfully applied to a trifurcating organic reaction, a carbocation rearrangement and the solvent-dependent Pummerer-like reactions, demonstrating the power of the algorithm to analyse complex reactions. In Chapter 2, the ‘ValleyRidge’ algorithm has been extended to the ‘VRAI-Selectivity’ algorithm to model selectivities controlled by reaction dynamics rather than the transition state theory. Such reactions are difficult to analyse using the transition state theory because the approach often does not capture the subtlety of the energy landscapes the reaction trajectories traverse. Therefore, the transition state theory cannot accurately predict selectivities. The upgraded ‘VRAI-Selectivity’ algorithm can predict the major product and the selectivity for a wide range of potential energy surfaces where product distributions are influenced by reaction dynamics. The method requires the transition states, the intermediate (if present) and the product geometries from the reaction profile as the calculation input. The algorithm is quick and simple to run and, except for the two reactions with long alkyl chains, calculates selectivity more accurately than the transition state theory alone. The use of machine learning techniques in computational chemistry has gained momentum since large molecular databases are now readily available. The predictions of molecular properties with the trained machine learning models are computationally less expensive than the traditional quantum mechanics calculations without the loss of accuracy in many cases. In Chapter 3, a new explainable molecular representation based on bonds, angles and dihedrals has been developed. The machine learning models trained with this representation can accurately predict the electronic energies and the free energies of small organic molecules with atom types C, H N and O, with the mean absolute error of 1.2 kcal mol-1. The models are robust to extrapolations to larger organic molecules with the average error of less than 3.7 kcal mol-1 for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than the previous ML models of similar accuracy, have been achieved by sampling geometries around the potential energy surface minima. The structures around the minima have been sampled by randomly distorting the structures in the dataset. Therefore, the input geometries do not have to be fully optimised; accurate density functional theory electronic energy predictions can be made from force-field optimised geometries with the mean absolute error of 2.5 kcal mol-1. Chapter 4 combines the selectivity analysis concepts from Chapters 1 and 2 and the database-based approaches from Chapter 3. At present, no cheminformatic study has investigated how frequently the nonstatistical dynamics-driven selectivity might be observed. Chapter 4 investigates a Diels-Alder reaction that has many potential synthetic applications but shows selectivity controlled by nonstatistical dynamics. A new workflow has been developed that can automate the transition state optimisations and the reaction profile calculations. Many new variant Diels-Alder energy profiles have been generated through this workflow. Out of the 260 full reaction profiles calculated, 173 reactions could potentially have the selectivity governed by nonstatistical dynamics. Automating the transition state optimisationsand the selectivity predictions lead to a much wider chemical reaction space exploration. Chapter 4 illustrates how the cheminformatics and the molecular modelling approaches can be combined to investigate the origins of the selectivities in key organic reactions.Trinity Henry-Barlow Scholarshi

VRAI-selectivity: calculation of selectivity beyond transition state theory.

In recent years, a growing number of organic reactions in the literature have shown selectivity controlled by reaction dynamics rather than by transition state theory. Such reactions are difficult to analyse because the transition state theory approach often does not capture the subtlety of the energy landscapes the compounds traverse and, therefore, cannot accurately predict the selectivity. We present an algorithm that can predict the major product and selectivity for a wide range of potential energy surfaces where the product distribution is influenced by reaction dynamics. The method requires as input calculation of the transition states, the intermediate (if present) and the product geometries. The algorithm is quick and simple to run and, except for two reactions with long alkyl chains, calculates selectivity more accurately than transition state theory alone.Trinity College, University of Cambridge Cambridge Service for Data 24 Driven Discovery (CSD3

Research data supporting "VRAI-Selectivity: Calculation of Selectivity Beyond Transition State Theory"

3D coordinates of the geometries optimised for VRAI-Selectivity Python code development. Please see README.txt file for the detailsWe are grateful to Trinity College, University of Cambridge, the Leverhulme Trust (KE) and Isaac Newton Trust (KE) for their financial support of this research. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3), operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac. uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1) and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk)

Research data supporting 'Rapid Route-Finding for Bifurcating Organic Reactions'

This dataset contains: - Cartesian coordinates of all the optimised structures in the publication. Please see README document for more detailed informationWe are grateful to Trinity College, University of Cambridge for the financial support for this research