Hilbert-curve assisted structure embedding method.

MOTIVATION: Chemical space embedding methods are widely utilized in various research settings for dimensional reduction, clustering and effective visualization. The maps generated by the embedding process can provide valuable insight to medicinal chemists in terms of the relationships between structural, physicochemical and biological properties of compounds. However, these maps are known to be difficult to interpret, and the ''landscape'' on the map is prone to ''rearrangement'' when embedding different sets of compounds. RESULTS: In this study we present the Hilbert-Curve Assisted Space Embedding (HCASE) method which was designed to create maps by organizing structures according to a logic familiar to medicinal chemists. First, a chemical space is created with the help of a set of ''reference scaffolds''. These scaffolds are sorted according to the medicinal chemistry inspired Scaffold-Key algorithm found in prior art. Next, the ordered scaffolds are mapped to a line which is folded into a higher dimensional (here: 2D) space. The intricately folded line is referred to as a pseudo-Hilbert-Curve. The embedding of a compound happens by locating its most similar reference scaffold in the pseudo-Hilbert-Curve and assuming the respective position. Through a series of experiments, we demonstrate the properties of the maps generated by the HCASE method. Subjects of embeddings were compounds of the DrugBank and CANVASS libraries, and the chemical spaces were defined by scaffolds extracted from the ChEMBL database. SCIENTIFIC CONTRIBUTION: The novelty of HCASE method lies in generating robust and intuitive chemical space embeddings that are reflective of a medicinal chemist's reasoning, and the precedential use of space filling (Hilbert) curve in the process. AVAILABILITY: https://github.com/ncats/hcase.

An approach to rapid distributed manufacturing of broad spectrum anti-viral griffithsin using cell-free systems to mitigate pandemics.

This study describes the cell-free biomanufacturing of a broad-spectrum antiviral protein, griffithsin (GRFT) such that it can be produced in microgram quantities with consistent purity and potency in less than 24 h. We demonstrate GRFT production using two independent cell-free systems, one plant and one microbial. Griffithsin purity and quality were verified using standard regulatory metrics. Efficacy was demonstrated in vitro against SARS-CoV-2 and HIV-1 and was nearly identical to that of GRFT expressed in vivo. The proposed production process is efficient and can be readily scaled up and deployed wherever a viral pathogen might emerge. The current emergence of viral variants of SARS-CoV-2 has resulted in frequent updating of existing vaccines and loss of efficacy for front-line monoclonal antibody therapies. Proteins such as GRFT with its efficacious and broad virus neutralizing capability provide a compelling pandemic mitigation strategy to promptly suppress viral emergence at the source of an outbreak.

Algorithm for the Pruning of Synthesis Graphs.

Synthesis route planning is in the core of chemical intelligence that will power the autonomous chemistry platforms. In this task, we rely on algorithms to generate possible synthesis routes with the help of retro- and forward-synthetic approaches. Generated synthesis routes can be merged into a synthesis graph which represents theoretical pathways to the target molecule. However, it is often required to modify a synthesis graph due to typical constraints. These constraints might include "undesirable substances", e.g., an intermediate that the chemist does not favor or substances that might be toxic. Consequently, we need to prune the synthesis graph by the elimination of such undesirable substances. Synthesis graphs can be represented as directed (not necessarily acyclic) bipartite graphs, and the pruning of such graphs in the light of a set of undesirable substances has been an open question. In this study, we present the Synthesis Graph Pruning (SGP) algorithm that addresses this question. The input to the SGP algorithm is a synthesis graph and a set of undesirable substances. Furthermore, information for substances is provided as metadata regarding their availability from the inventory. The SGP algorithm operates with a simple local rule set, in order to determine which nodes and edges need to be eliminated from the synthesis graph. In this study, we present the SGP algorithm in detail and provide several case studies that demonstrate the operation of the SGP algorithm. We believe that the SGP algorithm will be an essential component of computer aided synthesis planning.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

An approach to rapid distributed manufacturing of broad spectrum anti-viral griffithsin using cell-free systems to mitigate pandemics.

This study describes the cell-free biomanufacturing of a broad-spectrum antiviral protein, griffithsin (GRFT) such that it can be produced with consistent purity and potency in less than 24 hours. We demonstrate GRFT production using two independent cell-free systems, one plant and one microbial. Griffithsin purity and quality were verified using standard regulatory metrics. Efficacy was demonstrated in vitro against SARS-CoV-2 and HIV-1 and was nearly identical to that of GRFT expressed in vivo . The proposed production process is efficient and can be readily scaled up and deployed anywhere in the world where a viral pathogen might emerge. The current emergence of viral variants has resulted in frequent updating of existing vaccines and loss of efficacy for front-line monoclonal antibody therapies. Proteins such as GRFT with its efficacious and broad virus neutralizing capability provide a compelling pandemic mitigation strategy to promptly suppress viral emergence at the source of an outbreak.

Cross-Platform Bayesian Optimization System for Autonomous Biological Assay Development.

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

MOTIVATION: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. RESULTS: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. AVAILABILITY: https://neo4covid19.ncats.io.

A Perspective on Innovating the Chemistry Lab Bench.

Innovating on the design and function of the chemical bench remains a quintessential challenge of the ages. It requires a deep understanding of the important role chemistry plays in scientific discovery as well a first principles approach to addressing the gaps in how work gets done at the bench. This perspective examines how one might explore designing and creating a sustainable new standard for advancing automated chemistry bench itself. We propose how this might be done by leveraging recent advances in laboratory automation whereby integrating the latest synthetic, analytical and information technologies, and AI/ML algorithms within a standardized framework, maximizes the value of the data generated and the broader utility of such systems. Although the context of this perspective focuses on the design of advancing molecule of potential therapeutic value, it would not be a stretch to contemplate how such systems could be applied to other applied disciplines like advanced materials, foodstuffs, or agricultural product development.

Discovery of selective RIO2 kinase small molecule ligand.

We report the discovery and initial optimization of diphenpyramide and several of its analogs as hRIO2 kinase ligands. One of these analogs is the most selective hRIO2 ligand reported to date. Diphenpyramide is a Cyclooxygenase 1 and 2 inhibitor that was used as an anti-inflammatory agent. The RIO2 kinase affinity of diphenpyramide was discovered by serendipity while profiling of 13 marketed drugs on a large 456 kinase assay panel. The inhibition values also suggested a relative selectivity of diphenpyramide for RIO2 against the other kinases in the panel. Subsequently three available and eight newly synthesized analogs were assayed, one of which showed a 10 fold increased hRIO2 binding affinity. Additionally, this compound shows significantly better selectivity over assayed kinases, when compared to currently known RIO2 inhibitors. As RIO2 is involved in the biosynthesis of the ribosome and cell cycle regulation, our selective ligand may be useful for the delineation of the biological role of this kinase. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

A remote-controlled adaptive medchem lab: an innovative approach to enable drug discovery in the 21st Century.

This article describes our experiences in creating a fully integrated, globally accessible, automated chemical synthesis laboratory. The goal of the project was to establish a fully integrated automated synthesis solution that was initially focused on minimizing the burden of repetitive, routine, rules-based operations that characterize more established chemistry workflows. The architecture was crafted to allow for the expansion of synthetic capabilities while also providing for a flexible interface that permits the synthesis objective to be introduced and manipulated as needed under the judicious direction of a remote user in real-time. This innovative central synthesis suite is herein described along with some case studies to illustrate the impact such a system is having in expanding drug discovery capabilities.

Application of the Dakin-West reaction for the synthesis of oxazole-containing dual PPARalpha/gamma agonists.

An improved method for the preparation of a series of oxazole-containing dual PPARalpha/gamma agonists is described. A synthetic sequence utilizing a Dakin-West reaction was devised that allows for the introduction of the oxazole ring either late in the synthetic sequence via aminomalonate-derived chemistry or in pivotal SAR intermediates derived from aspartic acid.