Can easy chemistry produce complex, diverse, and novel molecules?

Building Block-Centric Approach to DNA-Encoded Library Design

DNA-encoded library technology grants access to nearly infinite opportunities to explore the chemical structure space for drug discovery. Successful navigation depends on the design and synthesis of libraries with appropriate physicochemical properties (PCPs) and structural diversity while aligning with practical considerations. To this end, we analyze combinatorial library design constraints including the number of chemistry cycles, bond construction strategies, and building block (BB) class selection in pursuit of ideal library designs. We compare two-cycle library designs (amino acid + carboxylic acid, primary amine + carboxylic acid) in the context of PCPs and chemical space coverage, given different BB selection strategies and constraints. We find that broad availability of amines and acids is essential for enabling the widest exploration of chemical space. Surprisingly, cost is not a driving factor, and virtually, the same chemical space can be explored with "budget" BBs.

One-Pot Oxidative Amidation of Aldehydes via the Generation of Nitrile Imine Intermediates

A one-pot procedure for the oxidative amidation of aldehydes via the in situ generation of reactive nitrile imine (NI) intermediates has been developed. Distinct from our progenitor processes, mechanistic and control experiments revealed that the NI undergoes rapid oxidation to an acyl diazene species, which then facilitates N-acylation of an amine. A range of substrates have been explored, including application in the synthesis of pharmaceutically relevant compounds.

Screening Ultra-Large Encoded Compound Libraries Leads to Novel Protein-Ligand Interactions and High Selectivity

The DNA-encoded library (DEL) discovery platform has emerged as a powerful technology for hit identification in recent years. It has become one of the major parallel workstreams for small molecule drug discovery along with other strategies such as HTS and data mining. For many researchers working in the DEL field, it has become increasingly evident that many hits and leads discovered via DEL screening bind to target proteins with unique and unprecedented binding modes. This Perspective is our attempt to analyze reports of DEL screening with the purpose of providing a rigorous and useful account of the binding modes observed for DEL-derived ligands with a focus on binding mode novelty.

Rhodium(III)-Catalyzed Remote Hydroamidation of Internal Alkenes via Chain Walking

Hydroamination of terminal alkenes represents a powerful and well-established way to introduce nitrogenous functionality to feedstock chemicals. Remote hydroamination reactions are far less known, and represent a way to functionalize unactivated C(sp3) centers distal to the site of the alkene. These transformations commonly take place via metal hydride-mediated chain walking, and as such, regioselectivity can be challenging. The remote introduction of amides is of particular interest due to their prevalence in pharmaceuticals. Herein we report a Rh(III)-catalyzed hydroamidation procedure to functionalize the terminal position of internal alkenes, using dioxazolones as amidation reagents and i-PrOH as a hydride source. The reaction proceeds with high yield and regioselectivity, and tolerates a variety of functionality. Regioconvergent synthesis of a single linear amide from a mixture of isomeric alkenes is demonstrated. Key to the development of this reaction was determining that inorganic bases poison the catalyst, and identifying a suitable trialkylamine replacement.

Biocatalysis in Drug Design: Engineered Reductive Aminases (RedAms) Are Used to Access Chiral Building Blocks with Multiple Stereocenters

Novel building blocks are in constant demand during the search for innovative bioactive small molecule therapeutics by enabling the construction of structure-activity-property-toxicology relationships. Complex chiral molecules containing multiple stereocenters are an important component in compound library expansion but can be difficult to access by traditional organic synthesis. Herein, we report a biocatalytic process to access a specific diastereomer of a chiral amine building block used in drug discovery. A reductive aminase (RedAm) was engineered following a structure-guided mutagenesis strategy to produce the desired isomer. The engineered RedAm (IR-09 W204R) was able to generate the (S,S,S)-isomer 3 in 45% conversion and 95% ee from the racemic ketone 2. Subsequent palladium-catalyzed deallylation of 3 yielded the target primary amine 4 in a 73% yield. This engineered biocatalyst was used at preparative scale and represents a potential starting point for further engineering and process development.

Regioselective and Enantioselective Copper-Catalyzed Hydroaminocarbonylation of Unactivated Alkenes and Alkynes

Given the prevalence of amide backbones in marketed pharmaceuticals and their ubiquity as critical binding units in natural peptides and proteins, it remains important to develop novel methods to construct amide bonds. We report here a general method for the anti-Markovnikov hydroaminocarbonylation of unactivated alkenes under mild conditions, using copper catalysis in combination with hydroxylamine electrophile reagents and poly(methylhydrosiloxane) (PMHS) as a cheap and environmentally friendly hydride source. The reaction tolerates a variety of functional groups and efficiently converts unactivated terminal alkenes, 1,1-disubstituted alkenes, and cyclic alkenes to the corresponding amides with exclusive anti-Markovnikov selectivity (and high enantioselectivities/diastereoselectivities). Additionally, with minimal modification of the reaction conditions, alkynes can also undergo tandem hydrogenation-hydroaminocarbonylation to alkyl amides.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

A vending machine for drug-like molecules - automated synthesis of virtual screening hits

As a result of high false positive rates in virtual screening campaigns, prospective hits must be synthesised for validation. When done manually, this is a time consuming and laborious process. Large "on-demand" virtual libraries (>7 × 10¹² members), suitable for preparation using capsule-based automated synthesis and commercial building blocks, were evaluated to determine their structural novelty. One sub-library, constructed from iSnAP capsules, aldehydes and amines, contains unique scaffolds with drug-like physicochemical properties. Virtual screening hits from this iSnAP library were prepared in an automated fashion for evaluation against Aedes aegypti and Phytophthora infestans. In comparison to manual workflows, this approach provided a 10-fold improvement in user efficiency. A streamlined method of relative stereochemical assignment was also devised to augment the rapid synthesis. User efficiency was further improved to 100-fold by downscaling and parallelising capsule-based chemistry on 96-well plates equipped with filter bases. This work demonstrates that automated synthesis consoles can enable the rapid and reliable preparation of attractive virtual screening hits from large virtual libraries.

Rhodium(III)-Catalyzed Anti-Markovnikov Hydroamidation of Unactivated Alkenes Using Dioxazolones as Amidating Reagents

The amide is one of the most prevalent functional groups in all of pharmaceuticals, and for this reason, reactions that introduce the amide moiety are of particular value. Intermolecular hydroamidation of alkenes remains an underexplored method for the synthesis of amide-containing compounds. The majority of hydroamidation procedures exhibit Markovnikov regioselectivity, while current methods for anti-Markovnikov hydroamidation are somewhat limited to activated alkene substrates or radical processes. Herein, we report a general method for the intermolecular anti-Markovnikov hydroamidation of unactivated alkenes under mild conditions, utilizing Rh(III) catalysis in conjunction with dioxazolone amidating reagents and isopropanol as an environmentally friendly hydride source. The reaction tolerates a wide range of functional groups and efficiently converts electron-deficient alkenes, styrenes, and 1,1-disubstituted alkenes, in addition to unactivated alkenes, to their corresponding linear amides. Mechanistic studies reveal a reversible rhodium hydride migratory insertion step, leading to exquisite selectivity for the anti-Markovnikov product.

The Time and Place for Nature in Drug Discovery

The case for a renewed focus on Nature in drug discovery is reviewed; not in terms of natural product screening, but how and why biomimetic molecules, especially those produced by natural processes, should deliver in the age of artificial intelligence and screening of vast collections both in vitro and in silico. The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. Nature's finite number of building blocks and their interactions necessarily reduce potential numbers of structures, yet these enable expansion of chemical space with their inherent diversity of physical characteristics, pertinent to property-based design. The feasible variations on natural motifs are considered and expanded to encompass pseudo-natural products, leading to the further logical step of harnessing bioprocessing routes to access them. Together, these offer opportunities for enhancing natural mimicry, thereby bringing innovation to drug synthesis exploiting the characteristics of natural recognition processes. The potential for computational guidance to help identifying binding commonalities in the route map is a logical opportunity to enable the design of tailored molecules, with a focus on "organic/biological" rather than purely "synthetic" structures. The design and synthesis of prototype structures should pay dividends in the disposition and efficacy of the molecules, while inherently enabling greener and more sustainable manufacturing techniques.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a "good" hit in the course of a screening campaign still rely on a lot of luck - which is within the reach of academics -, that the hit to lead process requires a lot of chemistry and that if public-private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches

Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.

Defining Levels of Automated Chemical Design

One application area of computational methods in drug discovery is the automated design of small molecules. Despite the large number of publications describing methods and their application in both retrospective and prospective studies, there is a lack of agreement on terminology and key attributes to distinguish these various systems. We introduce Automated Chemical Design (ACD) Levels to clearly define the level of autonomy along the axes of ideation and decision making. To fully illustrate this framework, we provide literature exemplars and place some notable methods and applications into the levels. The ACD framework provides a common language for describing automated small molecule design systems and enables medicinal chemists to better understand and evaluate such systems.

Amide Bond Formation via the Rearrangement of Nitrile Imines Derived from N-2-Nitrophenyl Hydrazonyl Bromides

We report how the rearrangement of highly reactive nitrile imines derived from N-2-nitrophenyl hydrazonyl bromides can be harnessed for the facile construction of amide bonds. This amidation reaction was found to be widely applicable to the synthesis of primary, secondary, and tertiary amides and was used as the key step in the synthesis of the lipid-lowering agent bezafibrate. The orthogonality and functional group tolerance of this approach was exemplified by the N-acylation of unprotected amino acids.

A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry

The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BBs). In this work, we present a detailed analysis of the chemical space of 400 000 purchasable BBs. The chemical space was defined by corresponding synthons─fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BBs were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space that enables visualization of both libraries and analysis of their overlapped and library-specific regions.

The Chosen Few: Parallel Library Reaction Methodologies for Drug Discovery

Parallel library synthesis is an important tool for drug discovery because it enables the synthesis of closely related analogues in parallel via robust and general synthetic transformations. In this perspective, we analyzed the synthetic methodologies used in >5000 parallel libraries representing 15 prevalent synthetic transformations. The library data set contains complex substrates and diverse arrays of building blocks used over the last 14 years at AbbVie. The library synthetic methodologies that have demonstrated robustness and generality with proven success are described along with their substrate scopes. The evolution of the synthetic methodologies for library synthesis over the past decade is discussed. We also highlight that the combination of parallel library synthesis with high-throughput experimentation will continue to facilitate the discovery of library-amenable synthetic methodologies in drug discovery.

SynthI: A New Open-Source Tool for Synthon-Based Library Design

Most of the existing computational tools for de novo library design are focused on the generation, rational selection, and combination of promising structural motifs to form members of the new library. However, the absence of a direct link between the chemical space of the retrosynthetically generated fragments and the pool of available reagents makes such approaches appear as rather theoretical and reality-disconnected. In this context, here we present Synthons Interpreter (SynthI), a new open-source toolkit for de novo library design that allows merging those two chemical spaces into a single synthons space. Here synthons are defined as actual fragments with valid valences and special labels, specifying the position and the nature of reactive centers. They can be issued from either the "breakup" of reference compounds according to 38 retrosynthetic rules or real reagents, after leaving group withdrawal or transformation. Such an approach not only enables the design of synthetically accessible libraries and analog generation but also facilitates reagents (building blocks) analysis in the medicinal chemistry context. SynthI code is publicly available at https://github.com/Laboratoire-de-Chemoinformatique/SynthI.

Photocatalysis in the Life Science Industry

In the pursuit of new pharmaceuticals and agrochemicals, chemists in the life science industry require access to mild and robust synthetic methodologies to systematically modify chemical structures, explore novel chemical space, and enable efficient synthesis. In this context, photocatalysis has emerged as a powerful technology for the synthesis of complex and often highly functionalized molecules. This Review aims to summarize the published contributions to the field from the life science industry, including research from industrial-academic partnerships. An overview of the synthetic methodologies developed and strategic applications in chemical synthesis, including peptide functionalization, isotope labeling, and both DNA-encoded and traditional library synthesis, is provided, along with a summary of the state-of-the-art in photoreactor technology and the effective upscaling of photocatalytic reactions.

Uniting Amide Synthesis and Activation by PIII/PV-Catalyzed Serial Condensation: Three-Component Assembly of 2-Amidopyridines

An organophosphorus (P^III/P^V redox) catalyzed method for the three-component condensation of amines, carboxylic acids, and pyridine N-oxides to generate 2-amidopyridines via serial dehydration is reported. Whereas amide synthesis and functionalization usually occur under divergent reaction conditions, here a phosphetane catalyst (together with a mild bromenium oxidant and terminal hydrosilane reductant) is shown to drive both steps chemoselectively in an auto-tandem catalytic cascade. The ability to both prepare and functionalize amides under the action of a single organocatalytic reactive intermediate enables new possibilities for the efficient and modular preparation of medicinal targets.

MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

Automated and enabling technologies for medicinal chemistry

Having always been driven by the need to get new treatments to patients as quickly as possible, drug discovery is a constantly evolving process. This chapter will review how medicinal chemistry was established, how it has changed over the years due to the emergence of new enabling technologies, and how early advances in synthesis, purification and analysis, have provided the foundations upon which the current automated and enabling technologies are built. Looking beyond the established technologies, this chapter will also consider technologies that are now emerging, and their impact on the future of drug discovery and the role of medicinal chemists.

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.

Design concepts for DNA-encoded library synthesis

An approach of building block (BB) inclusivity and atom efficient library schemes deliver the quality and diversity of DNA-encoded libraries best suited for small molecule drug discovery. In this Perspective, we offer key learnings in DEL design from a decade's worth of DEL-driven screening.

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.