High-Throughput Optimization of Photochemical Reactions using Segmented-Flow Nanoelectrospray-Ionization Mass Spectrometry.

Within the realm of drug discovery, high-throughput experimentation techniques enable the rapid optimization of reactions and expedited generation of drug compound libraries for biological and pharmacokinetic evaluation. Herein we report the development of a segmented flow mass spectrometry-based platform to enable the rapid exploration of photoredox reactions for early-stage drug discovery. Specifically, microwell plate-based photochemical reaction screens were reformatted to segmented flow format to enable delivery to nanoelectrospray ionization-mass spectrometry analysis. This approach was demonstrated for the late-stage modification of complex drug scaffolds, as well as the subsequent structure-activity relationship evaluation of synthesized analogs. This technology is anticipated to expand the robust capabilities of photoredox catalysis in drug discovery by enabling high-throughput library diversification.

Use of Green Solvents in Metallaphotoredox Cross-Electrophile Coupling Reactions Utilizing a Lipophilic Modified Dual Ir/Ni Catalyst System.

Facilitating photoredox coupling reactions in process-friendly green solvents was achieved by the successful application of a dual Ir/Ni catalyst system with enhanced solubility properties. These photochemical reactions (specifically Br-Br sp2-sp3 cross electrophile coupling) are reported in a head to head comparison to the standard di-t-Bu bipyridine ligand Ir/Ni catalyst system. This presentation highlights the benefits of altering the solubility properties of the ligands used in the Ir/Ni dual catalyst.

Batch versus flow photochemistry: a revealing comparison of yield and productivity.

The use of flow photochemistry and its apparent superiority over batch has been reported by a number of groups in recent years. To rigorously determine whether flow does indeed have an advantage over batch, a broad range of synthetic photochemical transformations were optimized in both reactor modes and their yields and productivities compared. Surprisingly, yields were essentially identical in all comparative cases. Even more revealing was the observation that the productivity of flow reactors varied very little to that of their batch counterparts when the key reaction parameters were matched. Those with a single layer of fluorinated ethylene propylene (FEP) had an average productivity 20% lower than that of batch, whereas three-layer reactors were 20% more productive. Finally, the utility of flow chemistry was demonstrated in the scale-up of the ring-opening reaction of a potentially explosive [1.1.1] propellane with butane-2,3-dione.

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery.

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

Identification of Novel Inhibitors of UDP-Galactopyranose Mutase by Structure-Based Virtual Screening.

UDP-galactopyranose mutase (UGM) is a flavo-enzyme involved in the bacterial cell wall biosynthesis. UGM catalyzes the reversible isomerization of UDP-galactopyranose (UDP-Galp) to UDP-galactofuranose (UDP-Galf). UDP-Galf is the activated precursor of galactofuranose (Galf) residues that are essential components of the cell wall of certain pathogenic bacteria such as Klebsiella pneumoniae and Mycobacterium tuberculosis. Neither UGM nor Galf residues are found in humans, making Galf biosynthesis a potential drug target for developing antibacterial agents. We report the identification of novel inhibitors of UGM by in silico docking of the LeadQuest compound database against UGM from Escherichia coli. The 13 most promising inhibitors were then evaluated against K. pneumonia and M. tuberculosis UGMs by enzyme inhibition studies. Two inhibitors were identified with IC50 values of ∼1 µM and subsequently these compounds were docked into the recently solved X-ray structure of Deinococcus radiodurans UGM. The structure-activity relationships of the initial 13 compounds evaluated as inhibitors are discussed.