Automation of Synthesis in Medicinal Chemistry: Progress and Challenges

An Automated Electrochemical Flow Platform to Accelerate Library Synthesis and Reaction Optimization

Automated batch and flow setups are well-established for high throughput experimentation in both thermal chemistry and photochemistry. However, the development of automated electrochemical platforms is hindered by cell miniaturization challenges in batch and difficulties in designing effective single-pass flow systems. In order to address these issues, we have designed and implemented a new, slug-based automated electrochemical flow platform. This platform was successfully demonstrated for electrochemical C-N cross-couplings of E3 ligase binders with diverse amines (44 examples), which were subsequently transferred to a continuous-flow mode for confirmation and isolation, showing its applicability for medicinal chemistry purposes. To further validate the versatility of the platform, Design of Experiments (DoE) optimization was performed for an unsuccessful library target. This optimization process, fully automated by the platform, resulted in a remarkable 6-fold increase in reaction yield.

An Automated Purification Workflow Coupled with Material-Sparing High-Throughput 1H NMR for Parallel Medicinal Chemistry

In medicinal chemistry, purification and characterization of organic compounds is an ever-growing challenge, with an increasing number of compounds being synthesized at a decreased scale of preparation. In response to this trend, we developed a parallel medicinal chemistry (PMC)-tailored platform, coupling automated purification to mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) on a range of synthetic scales (∼3.0-75.0 μmol). Here, the generation and acquisition of 1.7 mm NMR samples is fully integrated into a high-throughput automated workflow, processing 36 000 compounds yearly. Utilizing dead volume, which is inaccessible in conventional liquid handling, NMR samples are generated on as little as 10 μg without consuming material prioritized for biological assays. As miniaturized PMC synthesis becomes the industry standard, we can now obtain quality NMR spectra from limited material. Paired with automated structure verification, this platform has the potential to allow NMR to become as important for high-throughput analysis as ultrahigh performance liquid chromatography (UPLC)-MS.

Late-Stage C(sp2)-C(sp3) Diversification via Nickel Oxidative Addition Complexes

Herein, we describe nickel oxidative addition complexes (Ni-OACs) of drug-like molecules as a platform to rapidly generate lead candidates with enhanced C(sp3) fraction. The potential of Ni-OACs to access new chemical space has been assessed not only in C(sp2)-C(sp3) couplings but also in additional bond formations without recourse to specialized ligands and with improved generality when compared to Ni-catalyzed reactions. The development of an automated diversification process further illustrates the robustness of Ni-OACs, thus offering a new gateway to expedite the design-make-test-analyze (DMTA) cycle in drug discovery.

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients

Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.

Automated Synthesis for the Safe Production of Organic Azides from Primary Amines

Described herein is the development of an automated and reproducible process for the conversion of primary amines to organic azides utilizing prepacked capsules containing all the required reagents, including imidazole-1-sulfonyl azide tetrafluoroborate. Apart from manually loading the primary amine into the reaction vessel, the entire reaction and product isolation process can be achieved automatically, with no further user involvement, and delivers the desired organic azide in high purity. This practical and simple automated capsule-based method offers a convenient and safe way of generating organic azides without handling or exposure of potentially explosive reagents.

Establishing and testing a robot-based platform to enable the automated production of nanoparticles in a flexible and modular way

Robotic systems facilitate relatively simple human-robot interaction for non-robot experts, providing the flexibility to implement different processes. In this context, shorter process times, as well as an increased product and process quality could be achieved. Robots short time-consuming processes, take over ergonomically unfavorable tasks and work efficiently all the time. In addition, flexible production is possible while maintaining or even increasing safety. This study describes the successful development of a dual-arm robot-based modular infrastructure and the establishment of an automated process for the reproducible production of nanoparticles. As proof of concept, a manual synthesis protocol for silica nanoparticle preparation with a diameter of about 200 nm as building blocks for photonic crystals was translated into a fully automated process. All devices and components of the automated system were optimized and adapted according to the synthesis requirements. To demonstrate the benefit of the automated nanoparticle production, manual (synthesis done by lab technicians) and automated syntheses were benchmarked. To this end, different processing parameters (time of synthesis procedure, accuracy of dosage etc.) and the properties of the produced nanoparticles were compared. We demonstrate that the use of the robot not only increased the synthesis accuracy and reproducibility but reduced the personnel time and costs up to 75%.

A practical "preTACs-cytoblot" platform accelerates the streamlined development of PROTAC-based protein degraders

With the growing importance of PROTAC-mediated protein degradation in drug discovery, robust synthetic methodologies and rapid screening assays are urgently needed. By harnessing the improved alkene hydroazidation reaction, we developed a novel strategy to introduce azido groups into the linker-E3 ligand conjugates and effectively created a range of prepacked terminal azide-labeled "preTACs" as PROTAC toolkit building blocks. Moreover, we demonstrated that preTACs are ready to conjugate to ligands targeting a protein of interest to generate libraries of chimeric degraders, which are subsequently screened for effective protein degradation directly from cultured cells with a cytoblot assay. Our study exemplifies that this practical "preTACs-cytoblot" platform allows efficient PROTAC assembly and rapid activity assessments. It may help industrial and academic investigators to accelerate their streamlined development of PROTAC-based protein degraders.

Automation isn't automatic

Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that execute common laboratory procedures including parallel synthesis, reaction discovery, reaction optimization, time course studies, and crystallization development. While such systems offer many potential benefits, their implementation is rarely automatic due to the highly specialized nature of synthetic procedures. Each reaction category requires careful execution of a particular sequence of steps, the specifics of which change with different conditions and chemical systems. Careful assessment of these critical procedural requirements and identification of the tools suitable for effective experimental execution are key to developing effective automation workflows. Even then, it is often difficult to get all the components of an automated system integrated and operational. Data flows and specialized equipment present yet another level of challenge. Unfortunately, the pain points and process of implementing automated systems are often not shared or remain buried deep in the SI. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. Importantly, we aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and constant adjustment.

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

Inferring experimental procedures from text-based representations of chemical reactions

The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases.