Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries

Crystallographic fragment-binding studies of the Mycobacterium tuberculosis trifunctional enzyme suggest binding pockets for the tails of the acyl-CoA substrates at its active sites and a potential substrate-channeling path between them

The Mycobacterium tuberculosis trifunctional enzyme (MtTFE) is an α2β2 tetrameric enzyme in which the α-chain harbors the 2E-enoyl-CoA hydratase (ECH) and 3S-hydroxyacyl-CoA dehydrogenase (HAD) active sites, and the β-chain provides the 3-ketoacyl-CoA thiolase (KAT) active site. Linear, medium-chain and long-chain 2E-enoyl-CoA molecules are the preferred substrates of MtTFE. Previous crystallographic binding and modeling studies identified binding sites for the acyl-CoA substrates at the three active sites, as well as the NAD binding pocket at the HAD active site. These studies also identified three additional CoA binding sites on the surface of MtTFE that are different from the active sites. It has been proposed that one of these additional sites could be of functional relevance for the substrate channeling (by surface crawling) of reaction intermediates between the three active sites. Here, 226 fragments were screened in a crystallographic fragment-binding study of MtTFE crystals, resulting in the structures of 16 MtTFE-fragment complexes. Analysis of the 121 fragment-binding events shows that the ECH active site is the `binding hotspot' for the tested fragments, with 41 binding events. The mode of binding of the fragments bound at the active sites provides additional insight into how the long-chain acyl moiety of the substrates can be accommodated at their proposed binding pockets. In addition, the 20 fragment-binding events between the active sites identify potential transient binding sites of reaction intermediates relevant to the possible channeling of substrates between these active sites. These results provide a basis for further studies to understand the functional relevance of the latter binding sites and to identify substrates for which channeling is crucial.

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.

DrugGym: A testbed for the economics of autonomous drug discovery

Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.

Covalent hits and where to find them

Covalent hits for drug discovery campaigns are neither fantastic beasts nor mythical creatures, they can be routinely identified through electrophile-first screening campaigns using a suite of different techniques. These include biophysical and biochemical methods, cellular approaches, and DNA-encoded libraries. Employing best practice, however, is critical to success. The purpose of this review is to look at state of the art covalent hit identification, how to identify hits from a covalent library and how to select compounds for medicinal chemistry programmes.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Fragment-to-Lead Medicinal Chemistry Publications in 2022

This Perspective is the eighth in an annual series that summarizes successful fragment-to-lead (F2L) case studies published each year. A tabulated summary of relevant articles published in 2022 is provided, and features such as target class, screening methods, and ligand efficiency are discussed both for the 2022 examples and for the combined examples over the years 2015-2022. In addition, trends and new developments in the field are summarized. In 2022, 18 publications described successful fragment-to-lead studies, including the development of three clinical compounds (MTRX1719, MK-8189, and BI-823911).

Covalent fragment libraries in drug discovery-Design, synthesis, and screening methods

As the development of drugs with a covalent mode of action is becoming increasingly popular, well-validated covalent fragment-based drug discovery (FBDD) methods have been comparatively slow to keep up with the demand. In this chapter the principles of covalent fragment reactivity, library design, synthesis, and screening methods are explored in depth, focussing on literature examples with direct applications to practical covalent fragment library design and screening. Further, questions about the future of the field are explored and potential useful advances are proposed.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Covalent fragment approaches targeting non-cysteine residues

Covalent fragment approaches combine advantages of covalent binders and fragment-based drug discovery (FBDD) for target identification and validation. Although early applications focused mostly on cysteine labeling, the chemistries of available warheads that target other orthosteric and allosteric protein nucleophiles has recently been extended. The range of different warheads and labeling chemistries provide unique opportunities for screening and optimizing warheads necessary for targeting non-cysteine residues. In this review, we discuss these recently developed amino-acid-specific and promiscuous warheads, as well as emerging labeling chemistries, which includes novel transition metal catalyzed, photoactive, electroactive, and noncatalytic methodologies. We also highlight recent applications of covalent fragments for the development of molecular glues and proteolysis-targeting chimeras (PROTACs), and their utility in chemical proteomics-based target identification and validation.

Crystallographic fragment screening in academic cancer drug discovery

Fragment-based drug discovery (FBDD) has brought several drugs to the clinic, notably to target proteins once considered to be challenging, or even undruggable. Screening in FBDD relies upon observing and/or measuring weak (millimolar-scale) binding events using biophysical techniques or crystallographic fragment screening. This latter structural approach provides no information about binding affinity but can reveal binding mode and atomic detail on protein-fragment interactions to accelerate hit-to-lead development. In recent years, high-throughput platforms have been developed at synchrotron facilities to screen thousands of fragment-soaked crystals. However, using accessible manual techniques it is possible to run informative, smaller-scale screens within an academic lab setting. This chapter describes general protocols for home laboratory-scale fragment screening, from fragment soaking through to structure solution and, where appropriate, signposts to background, protocols or alternatives elsewhere.

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search

Fragment merging is a promising approach to progressing fragments directly to on-scale potency: each designed compound incorporates the structural motifs of overlapping fragments in a way that ensures compounds recapitulate multiple high-quality interactions. Searching commercial catalogues provides one useful way to quickly and cheaply identify such merges and circumvents the challenge of synthetic accessibility, provided they can be readily identified. Here, we demonstrate that the Fragment Network, a graph database that provides a novel way to explore the chemical space surrounding fragment hits, is well-suited to this challenge. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four crystallographic screening campaigns and contrast the results with a traditional fingerprint-based similarity search. The two approaches identify complementary sets of merges that recapitulate the observed fragment-protein interactions but lie in different regions of chemical space. We further show our methodology is an effective route to achieving on-scale potency by retrospective analyses for two different targets; in analyses of public COVID Moonshot and Mycobacterium tuberculosis EthR inhibitors, potential inhibitors with micromolar IC₅₀ values were identified. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search.

Mining the Protein Data Bank to inspire fragment library design

The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).

KID: A Kinase-Focused Interaction Database and Its Application in the Construction of Kinase-Focused Molecule Databases

Protein kinases are important drug targets for the treatment of several diseases. The interaction between kinases and ligands is vital in the process of small-molecule kinase inhibitor (SMKI) design. In this study, we propose a method to extract fragments and amino acid residues from crystal structures for kinase-ligand interactions. In addition, core fragments that interact with the important hinge region of kinases were extracted along with their decorations. Based on the superimposed structural data of kinases from the kinase-ligand interaction fingerprint and structure database, we obtained two libraries, namely, a hinge-unfocused fragment-amino acid pair library (FAP Lib) that contains 6672 pairs of fragments and corresponding amino-acids, and a hinge-focused hinge binder library (HB Lib) of 3560 pairs of hinge-binding scaffolds with their corresponding decorations. These two libraries constitute a kinase-focused interaction database (KID). In depth analysis was conducted on KID to explore important characteristics of fragments in the design of SMKIs. With KID, we built two kinase-focused molecule databases, one called Recomb_DB, which contains 1,72,346 molecules generated through fragment recombination based on the FAP Lib, and another called RsdHB_DB, which contains 93,030 molecules generated based on our HB Lib using molecular generation methods. Compared with five databases both commercial and non-commercial, these two databases both ranked top 3 in scaffold diversity, top 4 in molecule fingerprint diversity, and are more focused on the chemical space of kinase inhibitors. Hence, KID presents a useful addition to existing databases for the exploration of novel SMKIs.

Reactivity of Covalent Fragments and Their Role in Fragment Based Drug Discovery

Fragment based drug discovery has long been used for the identification of new ligands and interest in targeted covalent inhibitors has continued to grow in recent years, with high profile drugs such as osimertinib and sotorasib gaining FDA approval. It is therefore unsurprising that covalent fragment-based approaches have become popular and have recently led to the identification of novel targets and binding sites, as well as ligands for targets previously thought to be 'undruggable'. Understanding the properties of such covalent fragments is important, and characterizing and/or predicting reactivity can be highly useful. This review aims to discuss the requirements for an electrophilic fragment library and the importance of differing warhead reactivity. Successful case studies from the world of drug discovery are then be examined.