Twenty years on: the impact of fragments on drug discovery

Synthesis of Complex Tetracyclic Fused Scaffolds Enabled by (3 + 2) Cycloaddition

We describe the single-step formation of complex tetracyclic fused scaffolds enabled by (3 + 2) cycloaddition of azomethine ylides. Various indoles, N-protecting groups, and amino acids are well tolerated. The products are obtained in a catalyst-free manner with moderate to excellent yield and high diastereoselectivity. Representing a new scaffold that is not yet found in nature, the construction of pyrrolidine-fused cyclohepta-, azepino-, or oxepinoindoles could be found valuable in the synthesis of new pseudo-natural products.

t-SMILES: a fragment-based molecular representation framework for de novo ligand design

Effective representation of molecules is a crucial factor affecting the performance of artificial intelligence models. This study introduces a flexible, fragment-based, multiscale molecular representation framework called t-SMILES (tree-based SMILES) with three code algorithms: TSSA (t-SMILES with shared atom), TSDY (t-SMILES with dummy atom but without ID) and TSID (t-SMILES with ID and dummy atom). It describes molecules using SMILES-type strings obtained by performing a breadth-first search on a full binary tree formed from a fragmented molecular graph. Systematic evaluations using JTVAE, BRICS, MMPA, and Scaffold show the feasibility of constructing a multi-code molecular description system, where various descriptions complement each other, enhancing the overall performance. In addition, it can avoid overfitting and achieve higher novelty scores while maintaining reasonable similarity on labeled low-resource datasets, regardless of whether the model is original, data-augmented, or pre-trained then fine-tuned. Furthermore, it significantly outperforms classical SMILES, DeepSMILES, SELFIES and baseline models in goal-directed tasks. And it surpasses state-of-the-art fragment, graph and SMILES based approaches on ChEMBL, Zinc, and QM9.

Protein engineering enables a soakable crystal form of human CDK7 primed for high-throughput crystallography and structure-based drug design

Cyclin dependent kinase 7 (CDK7) is an important therapeutic kinase best known for its dual role in cell cycle regulation and gene transcription. Here, we describe the application of protein engineering to generate constructs leading to high resolution crystal structures of human CDK7 in both active and inactive conformations. The active state of the kinase was crystallized by incorporation of an additional surface residue mutation (W132R) onto the double phosphomimetic mutant background (S164D and T170E) that yielded the inactive kinase structure. A novel back-soaking approach was developed to determine crystal structures of several clinical and pre-clinical inhibitors of this kinase, demonstrating the potential utility of the crystal system for structure-based drug design (SBDD). The crystal structures help to rationalize the mode of inhibition and the ligand selectivity profiles versus key anti-targets. The protein engineering approach described here illustrates a generally applicable strategy for structural enablement of challenging molecular targets.

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

Optimizing NMR fragment-based drug screening for membrane protein targets

NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR-especially human membrane proteins-and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D6-DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

HEIDI: an experiment-management platform enabling high-throughput fragment and compound screening

The Swiss Light Source facilitates fragment-based drug-discovery campaigns for academic and industrial users through the Fast Fragment and Compound Screening (FFCS) software suite. This framework is further enriched by the option to utilize the Smart Digital User (SDU) software for automated data collection across the PXI, PXII and PXIII beamlines. In this work, the newly developed HEIDI webpage (https://heidi.psi.ch) is introduced: a platform crafted using state-of-the-art software architecture and web technologies for sample management of rotational data experiments. The HEIDI webpage features a data-review tab for enhanced result visualization and provides programmatic access through a representational state transfer application programming interface (REST API). The migration of the local FFCS MongoDB instance to the cloud is highlighted and detailed. This transition ensures secure, encrypted and consistently accessible data through a robust and reliable REST API tailored for the FFCS software suite. Collectively, these advancements not only significantly elevate the user experience, but also pave the way for future expansions and improvements in the capabilities of the system.

Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development

Metal-based complexes with their unique chemical properties, including multiple oxidation states, radio-nuclear capabilities and various coordination geometries yield value as potential pharmaceuticals. Understanding the interactions between metals and biological systems will prove key for site-specific coordination of new metal-based lead compounds. This study merges the concepts of target coordination with fragment-based drug methodologies, supported by varying the anomalous scattering of rhenium along with infrared spectroscopy, and has identified rhenium metal sites bound covalently with two amino acid types within the model protein. A time-based series of lysozyme-rhenium-imidazole (HEWL-Re-Imi) crystals was analysed systematically over a span of 38 weeks. The main rhenium covalent coordination is observed at His15, Asp101 and Asp119. Weak (i.e. noncovalent) interactions are observed at other aspartic, asparagine, proline, tyrosine and tryptophan side chains. Detailed bond distance comparisons, including precision estimates, are reported, utilizing the diffraction precision index supplemented with small-molecule data from the Cambridge Structural Database. Key findings include changes in the protein structure induced at the rhenium metal binding site, not observed in similar metal-free structures. The binding sites are typically found along the solvent-channel-accessible protein surface. The three primary covalent metal binding sites are consistent throughout the time series, whereas binding to neighbouring amino acid residues changes through the time series. Co-crystallization was used, consistently yielding crystals four days after setup. After crystal formation, soaking of the compound into the crystal over 38 weeks is continued and explains these structural adjustments. It is the covalent bond stability at the three sites, their proximity to the solvent channel and the movement of residues to accommodate the metal that are important, and may prove useful for future radiopharmaceutical development including target modification.

A small step towards an important goal: fragment screen of the c-di-AMP-synthesizing enzyme CdaA

CdaA is the most widespread diadenylate cyclase in many bacterial species, including several multidrug-resistant human pathogens. The enzymatic product of CdaA, cyclic di-AMP, is a secondary messenger that is essential for the viability of many bacteria. Its absence in humans makes CdaA a very promising and attractive target for the development of new antibiotics. Here, the structural results are presented of a crystallographic fragment screen against CdaA from Listeria monocytogenes, a saprophytic Gram-positive bacterium and an opportunistic food-borne pathogen that can cause listeriosis in humans and animals. Two of the eight fragment molecules reported here were localized in the highly conserved ATP-binding site. These fragments could serve as potential starting points for the development of antibiotics against several CdaA-dependent bacterial species.

Biocatalytic strategy for the construction of sp3-rich polycyclic compounds from directed evolution and computational modelling

Catalysis with engineered enzymes has provided more efficient routes for the production of active pharmaceutical agents. However, the potential of biocatalysis to assist in early-stage drug discovery campaigns remains largely untapped. In this study, we have developed a biocatalytic strategy for the construction of sp3-rich polycyclic compounds via the intramolecular cyclopropanation of benzothiophenes and related heterocycles. Two carbene transferases with complementary regioisomer selectivity were evolved to catalyse the stereoselective cyclization of benzothiophene substrates bearing diazo ester groups at the C2 or C3 position of the heterocycle. The detailed mechanisms of these reactions were elucidated by a combination of crystallographic and computational analyses. Leveraging these insights, the substrate scope of one of the biocatalysts could be expanded to include previously unreactive substrates, highlighting the value of integrating evolutionary and rational strategies to develop enzymes for new-to-nature transformations. The molecular scaffolds accessed here feature a combination of three-dimensional and stereochemical complexity with 'rule-of-three' properties, which should make them highly valuable for fragment-based drug discovery campaigns.

ChemoDOTS: a web server to design chemistry-driven focused libraries

In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

Chemical screening by time-resolved X-ray scattering to discover allosteric probes

Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis (kVR), capture structure-activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF-aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF-aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states.

Design, quality and validation of the EU-OPENSCREEN fragment library poised to a high-throughput screening collection

The EU-OPENSCREEN (EU-OS) European Research Infrastructure Consortium (ERIC) is a multinational, not-for-profit initiative that integrates high-capacity screening platforms and chemistry groups across Europe to facilitate research in chemical biology and early drug discovery. Over the years, the EU-OS has assembled a high-throughput screening compound collection, the European Chemical Biology Library (ECBL), that contains approximately 100 000 commercially available small molecules and a growing number of thousands of academic compounds crowdsourced through our network of European and non-European chemists. As an extension of the ECBL, here we describe the computational design, quality control and use case screenings of the European Fragment Screening Library (EFSL) composed of 1056 mini and small chemical fragments selected from a substructure analysis of the ECBL. Access to the EFSL is open to researchers from both academia and industry. Using EFSL, eight fragment screening campaigns using different structural and biophysical methods have successfully identified fragment hits in the last two years. As one of the highlighted projects for antibiotics, we describe the screening by Bio-Layer Interferometry (BLI) of the EFSL, the identification of a 35 μM fragment hit targeting the beta-ketoacyl-ACP synthase 2 (FabF), its binding confirmation to the protein by X-ray crystallography (PDB 8PJ0), its subsequent rapid exploration of its surrounding chemical space through hit-picking of ECBL compounds that contain the fragment hit as a core substructure, and the final binding confirmation of two follow-up hits by X-ray crystallography (PDB 8R0I and 8R1V).

The Science and Art of Structure-Based Virtual Screening

Structure-based virtual screening has gained momentum again as the high attrition rate at every stage of drug discovery drives the need to explore a greater chemical space. From the Bayesian perspective, its shortcomings as a viable strategy for sustainable hit discovery are discussed, with regard to the prior hit rates of screening libraries and the performance of computational methods. Lessons are shared in selecting virtual hits for experimental validation learned from a series of eight successful campaigns, one of which impacted the discovery of a drug candidate currently in clinical trials.

Identification of BACE-1 inhibitors through directed C(sp3)-H activation on 5-oxo-pyrrolidine-3-carboxylic acid derivatives

Convenient synthesis of stereochemically dense 5-oxo-pyrrolidines was obtained from succinic anyhdride and imines by combining the Castagnoli-Cushman reaction with directed Pd-catalyzed C(sp3)-H functionalization, taking advantage of the developing carboxylic group properly derivatized with 8-aminoquinoline as a directing group. These fully substituted 5-oxopyrrolidines were found to be able to inhibit BACE-1 enzyme with sub-micromolar activity, thanks to the interaction of the key aryl appendage introduced by C(sp3)-H activation within BACE-1 S2' subsite.

Protein-templated ligand discovery via the selection of DNA-encoded dynamic libraries

DNA-encoded chemical libraries (DELs) have become a powerful technology platform in drug discovery. Dual-pharmacophore DELs display two sets of small molecules at the termini of DNA duplexes, thereby enabling the identification of synergistic binders against biological targets, and have been successfully applied in fragment-based ligand discovery and affinity maturation of known ligands. However, dual-pharmacophore DELs identify separate binders that require subsequent linking to obtain the full ligands, which is often challenging. Here we report a protein-templated DEL selection approach that can identify full ligand/inhibitor structures from DNA-encoded dynamic libraries (DEDLs) without the need for subsequent fragment linking. Our approach is based on dynamic DNA hybridization and target-templated in situ ligand synthesis, and it incorporates and encodes the linker structures in the library, along with the building blocks, to be sampled by the target protein. To demonstrate the performance of this method, 4.35-million- and 3.00-million-member DEDLs with different library architectures were prepared, and hit selection was achieved against four therapeutically relevant target proteins.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand‒receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and July 20, 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analysed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2,031 publications were included. These publications primarily originated from 99 countries or regions, led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modelling, pharmacophore modelling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics direction for future development. Furthermore, this paper will be helpful for better understanding the frontiers and hotspots of CADD.

A Novel NMR-Based Protocol to Screen Ultralow Molecular Weight Fragments

Fragment-based lead discovery has emerged as one of the most efficient screening strategies for finding hit molecules in drug discovery. Recently, a novel strategy based on a class of fragments characterized by an ultralow molecular weight (ULMW) has been proposed. These fragments bind to the target with a very low affinity, requiring reliable biophysical methods for detection. The most notable application of ULMW used a set of 81 fragments, named MiniFrags, and screened them by X-ray crystallography. We extended the utilization of this novel class of fragments to another gold standard technique for fragment-based screening: nuclear magnetic resonance (NMR). Here, we present a novel NMR protocol to detect and analyze such weak interactions in a challenging real-world scenario: a flexible target with a flat, water-exposed binding site. We identified a subset of 69 highly water-soluble MiniFrags that were screened against the antiapoptotic protein human Bfl-1.

Catalytic Protein Inhibitors

Many of the highest priority targets in a wide range of disease states are difficult-to-drug proteins. The development of reversible small molecule inhibitors for the active sites of these proteins with sufficient affinity and residence time on-target is an enormous challenge. This has engendered interest in strategies to increase the potency of a given protein inhibitor by routes other than further improvement in gross affinity. Amongst these, the development of catalytic protein inhibitors has garnered the most attention and investment, particularly with respect to protein degraders, which catalyze the destruction of the target protein. This article discusses the genesis of the burgeoning field of catalytic inhibitors, the current state of the art, and exciting future directions.

Fragment-Based Discovery of Allosteric Inhibitors of SH2 Domain-Containing Protein Tyrosine Phosphatase-2 (SHP2)

The ubiquitously expressed protein tyrosine phosphatase SHP2 is required for signaling downstream of receptor tyrosine kinases (RTKs) and plays a role in regulating many cellular processes. Genetic knockdown and pharmacological inhibition of SHP2 suppresses RAS/MAPK signaling and inhibit the proliferation of RTK-driven cancer cell lines. Here, we describe the first reported fragment-to-lead campaign against SHP2, where X-ray crystallography and biophysical techniques were used to identify fragments binding to multiple sites on SHP2. Structure-guided optimization, including several computational methods, led to the discovery of two structurally distinct series of SHP2 inhibitors binding to the previously reported allosteric tunnel binding site (Tunnel Site). One of these series was advanced to a low-nanomolar lead that inhibited tumor growth when dosed orally to mice bearing HCC827 xenografts. Furthermore, a third series of SHP2 inhibitors was discovered binding to a previously unreported site, lying at the interface of the C-terminal SH2 and catalytic domains.

Synthesis of Benzofuro[3,2-b]indol-3-one Derivatives via Dearomative (3 + 2) Cycloaddition of 2-Nitrobenzofurans and para-Quinamines

An efficient dearomative (3 + 2) cycloaddition of para-quinamines and 2-nitrobenzofurans has been developed. This reaction proceeds smoothly under mild conditions and affords a series of benzofuro[3,2-b]indol-3-one derivatives in good to excellent yields (up to 98%) with perfect diastereoselectivities (all cases > 20:1 dr). The scale-up synthesis and versatile derivatizations demonstrate the potential synthetic application of the protocol. A plausible reaction mechanism is also proposed to account for the observed reaction process. This work represents the first instance of the N-triggered dearomative (3 + 2) cycloaddition of 2-nitrobenzofurans.

LpxC inhibition: Potential and opportunities with carbohydrate scaffolds

Uridine diphosphate-3-O-(hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) is a key enzyme involved in the biosynthesis of lipid A, an essential building block, for the construction and assembly of the outer membrane (OM) of Gram-negative bacteria. The enzyme is highly conserved in almost all Gram-negative bacteria and hence has emerged as a promising target for drug discovery in the fight against multi-drug resistant Gram-negative infections. Since the first nanomolar LpxC inhibitor, L-161,240, an oxazoline-based hydroxamate, the two-decade-long ongoing search has provided valuable information regarding essential features necessary for inhibition. Although the design and structure optimization for arriving at the most efficacious inhibitor of this enzyme has made good use of different heterocyclic moieties, the use of carbohydrate scaffold is scant. This review briefly covers the advancement and progress made in LpxC inhibition. The field awaits the use of potential associated with carbohydrate-based scaffolds for LpxC inhibition and the discovery of anti-bacterial agents against Gram-negative infections.

Chemical tools to expand the ligandable proteome: diversity-oriented synthesis-based photoreactive stereoprobes

Chemical proteomics enables the global assessment of small molecule-protein interactions in native biological systems and has emerged as a versatile approach for ligand discovery. The range of small molecules explored by chemical proteomics has, however, been limited. Here, we describe a diversity-oriented synthesis (DOS)-inspired library of stereochemically-defined compounds bearing diazirine and alkyne units for UV light-induced covalent modification and click chemistry enrichment of interacting proteins, respectively. We find that these 'photo-stereoprobes' interact in a stereoselective manner with hundreds of proteins from various structural and functional classes in human cells and demonstrate that these interactions can form the basis for high-throughput screening-compatible nanoBRET assays. Integrated phenotypic analysis and chemical proteomics identified photo-stereoprobes that modulate autophagy by engaging the mitochondrial serine protease CLPP. Our findings show the utility of photo-stereoprobes for expanding the ligandable proteome, furnishing target engagement assays, and discovering and characterizing bioactive small molecules by cell-based screening.

The allure of targets for novel drugs

The challenges of bringing new medicines to patients have been extensively discussed and debated, including consideration of the contribution that academic laboratories can make. At the University of Strathclyde, drug discovery has been a continuing focal activity since the 1960s, and in the past 30 years, the author has led or contributed to many projects of different character and for diverse diseases. A feature common to these projects is the extension of concepts of molecular and biological targets in drug discovery research. In mechanistic terms, these have included compounds that are activators and not inhibitors, and in particular multitargeted compounds. With respect to relevance to disease, schizophrenia, pulmonary disfunction, autoimmune, and infectious disease are most relevant. These projects are discussed in the context of classical medicinal chemistry and more recent concepts in and approaches to drug discovery.

Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors

Bivalent molecules consisting of groups connected through bridging linkers often exhibit strong target binding and unique biological effects. However, developing bivalent inhibitors with the desired activity is challenging due to the dual motif architecture of these molecules and the variability that can be introduced through differing linker structures and geometries. We report a set of alternatively linked bivalent EGFR inhibitors that simultaneously occupy the ATP substrate and allosteric pockets. Crystal structures show that initial and redesigned linkers bridging a trisubstituted imidazole ATP-site inhibitor and dibenzodiazepinone allosteric-site inhibitor proved successful in spanning these sites. The re-engineered linker yielded a compound that exhibited significantly higher potency (~60 pM) against the drug-resistant EGFR L858R/T790M and L858R/T790M/C797S, which was superadditive as compared with the parent molecules. The enhanced potency is attributed to factors stemming from the linker connection to the allosteric-site group and informs strategies to engineer linkers in bivalent agent design.

Combined In-Solution Fragment Screening and Crystallographic Binding-Mode Analysis with a Two-Domain Hsp70 Construct

Heat shock protein 70 (Hsp70) isoforms are key players in the regulation of protein homeostasis and cell death pathways and are therefore attractive targets in cancer research. Developing nucleotide-competitive inhibitors or allosteric modulators, however, has turned out to be very challenging for this protein family, and no Hsp70-directed therapeutics have so far become available. As the field could profit from alternative starting points for inhibitor development, we present the results of a fragment-based screening approach on a two-domain Hsp70 construct using in-solution NMR methods, together with X-ray-crystallographic investigations and mixed-solvent molecular dynamics simulations. The screening protocol resulted in hits on both domains. In particular, fragment binding in a deeply buried pocket at the substrate-binding domain could be detected. The corresponding site is known to be important for communication between the nucleotide-binding and substrate-binding domains of Hsp70 proteins. The main fragment identified at this position also offers an interesting starting point for the development of a dual Hsp70/Hsp90 inhibitor.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

3D-QSAR-based pharmacophore modelling of quinazoline derivatives for the identification of acetylcholinesterase inhibitors through virtual screening, molecular docking, molecular dynamics and DFT studies

Alzheimer's disease (AD) is a progressive neurological disorder responsible for the cognitive dysfunction and cognitive impairment in the patients. Acetylcholinesterase inhibitors (AChEIs) are used to treat AD however, these only provided symptomatic relief and more efficient drug molecules are desired for the effective treatment of the disease. In this article, ligand-based drug-designing strategy was used to develop and validate a field-based 3D-QSAR pharmacophore model on quinazoline-based AChEIs reported in the literature. The validated pharmacophore model (AAAHR_1) was used as a prefilter to screen an ASINEX database via virtual screening workflow (VSW). The hits generated were subjected to MM-GBSA to identify potential AChEIs and top three scoring molecules (BAS 05264565, LEG 12727144 and SYN 22339886) were evaluated for thermodynamic stability at the target site using molecular dynamic simulations. Additionally, DFT study was performed to predict the reactivity of lead molecules towards acetylcholinesterase (AChE). Thus, by utilising various computational tools, three molecules were identified as potent AChEIs that can be developed as potential drug candidates for the treatment of AD.Communicated by Ramaswamy H. Sarma.

Identification of a Novel Subtype-Selective α1B-Adrenoceptor Antagonist

α1A-, α1B-, and α1D-adrenoceptors (α1-ARs) are members of the adrenoceptor G protein-coupled receptor family that are activated by adrenaline (epinephrine) and noradrenaline. α1-ARs are clinically targeted using antagonists that have minimal subtype selectivity, such as prazosin and tamsulosin, to treat hypertension and benign prostatic hyperplasia, respectively. Abundant expression of α1-ARs in the heart and central nervous system (CNS) makes these receptors potential targets for the treatment of cardiovascular and CNS disorders, such as heart failure, epilepsy, and Alzheimer's disease. Our understanding of the precise physiological roles of α1-ARs, however, and their involvement in disease has been hindered by the lack of sufficiently subtype-selective tool compounds, especially for α1B-AR. Here, we report the discovery of 4-[(2-hydroxyethyl)amino]-6-methyl-2H-chromen-2-one (Cpd1), as an α1B-AR antagonist that has 10-15-fold selectivity over α1A-AR and α1D-AR. Through computational and site-directed mutagenesis studies, we have identified the binding site of Cpd1 in α1B-AR and propose the molecular basis of α1B-AR selectivity, where the nonconserved V19745.52 residue plays a major role, with contributions from L3146.55 within the α1B-AR pocket. By exploring the structure-activity relationships of Cpd1 at α1B-AR, we have also identified 3-[(cyclohexylamino)methyl]-6-methylquinolin-2(1H)-one (Cpd24), which has a stronger binding affinity than Cpd1, albeit with reduced selectivity for α1B-AR. Cpd1 and Cpd24 represent potential leads for α1B-AR-selective drug discovery and novel tool molecules to further study the physiology of α1-ARs.

An 19F NMR fragment-based approach for the discovery and development of BRCA2-RAD51 inhibitors to pursuit synthetic lethality in combination with PARP inhibition in pancreatic cancer

The BRCA2-RAD51 interaction remains an intriguing target for cancer drug discovery due to its vital role in DNA damage repair mechanisms, which cancer cells become particularly reliant on. Moreover, RAD51 has many synthetically lethal partners, including PARP1-2, which can be exploited to induce synthetic lethality in cancer. In this study, we established a 19F-NMR-fragment based approach to identify RAD51 binders, leading to two initial hits. A subsequent SAR program identified 46 as a low micromolar inhibitor of the BRCA2-RAD51 interaction. 46 was tested in different pancreatic cancer cell lines, to evaluate its ability to inhibit the homologous recombination DNA repair pathway, mediated by BRCA2-RAD51 and trigger synthetic lethality in combination with the PARP inhibitor talazoparib, through the induction of apoptosis. Moreover, we further analyzed the 46/talazoparib combination in 3D pancreatic cancer models. Overall, 46 showed its potential as a tool to evaluate the RAD51/PARP1-2 synthetic lethality mechanism, along with providing a prospect for further inhibitors development.

Molecular fragmentation as a crucial step in the AI-based drug development pathway

The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.

Recent developments of P-glycoprotein inhibitors and its structure-activity relationship (SAR) studies

P-glycoprotein (P-gp) over-expression is a key factor in multi-drug resistance (MDR), which is a major factor in the failure of cancer treatment. P-gp inhibitors have been demonstrated to have powerful pharmacological properties and may be used as a therapeutic approach to overcome the MDR in cancer cells. Combining clinical investigations with biochemical and computational research may potentially lead to a clearer understanding of the pharmacological properties and the mechanisms of action of these P-gp inhibitors. The task of turning these discoveries into effective therapeutic candidates for a variety of malignancies, including resistant and metastatic kinds, falls on medicinal chemists. A variety of P-gp inhibitors with great potency, high selectivity, and minimal toxicity have been identified in recent years. The latest advances in drug design, characterization, structure-activity relationship (SAR) research, and modes of action of newly synthesized, powerful small molecules P-gp inhibitors over the previous ten years are highlighted in this review. P-gp transporter over-expression has been linked to MDR, therefore the development of P-gp inhibitors will expand our understanding of the processes and functions of P-gp-mediated drug efflux, which will be helpful for drug discovery and clinical cancer therapies.

Fragment library screening by X-ray crystallography and binding site analysis on thioredoxin glutathione reductase of Schistosoma mansoni

Schistosomiasis is caused by parasites of the genus Schistosoma, which infect more than 200 million people. Praziquantel (PZQ) has been the main drug for controlling schistosomiasis for over four decades, but despite that it is ineffective against juvenile worms and size and taste issues with its pharmaceutical forms impose challenges for treating school-aged children. It is also important to note that PZQ resistant strains can be generated in laboratory conditions and observed in the field, hence its extensive use in mass drug administration programs raises concerns about resistance, highlighting the need to search for new schistosomicidal drugs. Schistosomes survival relies on the redox enzyme thioredoxin glutathione reductase (TGR), a validated target for the development of new anti-schistosomal drugs. Here we report a high-throughput fragment screening campaign of 768 compounds against S. mansoni TGR (SmTGR) using X-ray crystallography. We observed 49 binding events involving 35 distinct molecular fragments which were found to be distributed across 16 binding sites. Most sites are described for the first time within SmTGR, a noteworthy exception being the "doorstop pocket" near the NADPH binding site. We have compared results from hotspots and pocket druggability analysis of SmTGR with the experimental binding sites found in this work, with our results indicating only limited coincidence between experimental and computational results. Finally, we discuss that binding sites at the doorstop/NADPH binding site and in the SmTGR dimer interface, should be prioritized for developing SmTGR inhibitors as new antischistosomal drugs.

Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening

Target-based discovery of first-in-class therapeutics demands an in-depth understanding of the molecular mechanisms underlying human diseases. Precise measurements of cellular and biochemical activities are critical to gain mechanistic knowledge of biomolecules and their altered function in disease conditions. Such measurements enable the development of intervention strategies for preventing or treating diseases by modulation of desired molecular processes. Fluorescence-based techniques are routinely employed for accurate and robust measurements of in-vitro activity of molecular targets and for discovering novel chemical molecules that modulate the activity of molecular targets. In the current review, the authors focus on the applications of fluorescence-based high throughput screening (HTS) and fragment-based ligand discovery (FBLD) techniques such as fluorescence polarization (FP), Förster resonance energy transfer (FRET), fluorescence thermal shift assay (FTSA) and microscale thermophoresis (MST) for the discovery of chemical probe to exploring target's role in disease biology and ultimately, serve as a foundation for drug discovery. Some recent advancements in these techniques for compound library screening against important classes of drug targets, such as G-protein-coupled receptors (GPCRs) and GTPases, as well as phosphorylation- and acetylation-mediated protein-protein interactions, are discussed. Overall, this review presents a landscape of how these techniques paved the way for the discovery of small-molecule modulators and biologics against these targets for therapeutic benefits.

Discovery of Five SOS2 Fragment Hits with Binding Modes Determined by SOS2 X-Ray Cocrystallography

SOS1 and SOS2 are guanine nucleotide exchange factors that mediate RTK-stimulated RAS activation. Selective SOS1:KRAS PPI inhibitors are currently under clinical investigation, whereas there are no reports to date of SOS2:KRAS PPI inhibitors. SOS2 activity is implicated in MAPK rebound when divergent SOS1 mutant cell lines are treated with the SOS1 inhibitor BI-3406; therefore, SOS2:KRAS inhibitors are of therapeutic interest. In this report, we detail a fragment-based screening strategy to identify X-ray cocrystal structures of five diverse fragment hits bound to SOS2.

Integration of biophysical and biological approaches to validate fragment-like compounds targeting l,d-transpeptidases from Mycobacterium tuberculosis

Tuberculosis is one of the major causes of death worldwide; more than a million people die every year because of this infection. The constant emergency of Mycobacterium tuberculosis resistant strains against the most used treatments also contributes to the burden caused by this disease. Consequently, the development of new alternative therapies against this disease is constantly required. In recent years, only a few molecules have reached the market as new antituberculosis agents. The mycobacterial cell wall biosynthesis is for a longstanding considered an important target for drug development. Particularly, in M. tuberculosis, the peptidoglycan cross-links are predominantly formed by nonclassical bridges between the third residues of adjacent tetrapeptides. The responsible enzymes for these reactions are ld-transpeptidases (Ldts), for which M. tuberculosis has five paralogues. Although these enzymes are distinct from the penicillin-binding proteins (PBPs), they can also be inactivated by β-lactam antibiotics, but since M. tuberculosis has a chromosomal β-lactamase, most of the antibiotics of these classes can be degraded. Thus, to identify alternative scaffolds for the development of new antimicrobials against tuberculosis, we have integrated several fragment-based drug discovery techniques. Based on that, we identified and validated a number of small molecules that could be the starting point in the synthesis of more potent inhibitors against at least two Ldts from M. tuberculosis, LdtMt2 and LdtMt3. Eight identified molecules inhibited the Ldts activity in at least 20%, and three of them have antimycobacterial activity. The cell ultrastructural analysis suggested that one of the best compounds induced severe effects on the septum and cell wall morphologies, which corroborates our target-based approach to identifying new Ldts hits.

Designing of fragment based inhibitors with improved activity against E. coli AmpC β-lactamase compared to the conventional antibiotics

One of the most common primary resistance mechanism of multi-drug resistant (MDR) Gram negative pathogenic bacteria to combat β-lactam antibiotics, such as penicillins, cephalosporins and carbapenems is the generation of β- lactamases. The uropathogenic E. coli is mostly getting multi-drug resistance due to the synthesis of AmpC β-lactamases and therefore new antibiotics and inhibitors are needed to treat the evolving infections. The current study was designed for targetting AmpC β-lactamase of E. coli using molecular docking based virtual screening, linking fragments for designing novel compounds and binding mode analysis using molecular dynamic simulation with target protein. The FCH group all-purpose fragment library consisting of 9388 fragments has been screened against AmpC β-lactamase protein of E. coli and the antibiotics and anti-infectives used in treatment of Urinary tract Infections (UTIs) were also screened with AmpC β-lactamase protein. Among the 9388 fragments, 339 fragment candidates were selected and linked with cefepime antibiotic having maximum binding affinity for AmpC target protein. Computational analysis of interactions as well as molecular dynamics (MD) simulations were also conducted for identifying the most promising ligand-pocket complexes from docking investigations to comprehend their thermodynamic properties and verify the docking outcomes as well. Overall, the linked complexes (LCs) showed good binding interactions with AmpC β-lactamase. Interestingly, our fragment-based LCs remained relatively stable in comparison with cefepime antibiotic. Moreover, S12 fragment linked complex remained the most stable during 50 ns with remarkable number of interactions indicating it as promising candidate in novel lead discovery against MDR E. coli infections.

Virtual screening and molecular dynamics simulation approach for the identification of potential multi-target directed ligands for the treatment of Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD, and multi-target directed ligands (MTDLs) are being explored as an alternative treatment strategy. Cholinesterase and monoamine oxidase enzymes are reported to play a crucial role in the pathology of AD, and multipotent ligands targeting these two enzymes simultaneously are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for identifying novel therapeutics. The current research work is focused on the development of potential multi-target directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing a structure-based virtual screening (SBVS) approach. The ASINEX database was screened after applying pan assay interference and drug-likeness filter to identify novel molecules using three docking precision criteria; High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP). Additionally, binding free energy calculations, ADME, and molecular dynamic simulations were employed to get structural insights into the mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified with binding scores of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In the near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.

Fragment-based drug nanoaggregation reveals drivers of self-assembly

Drug nanoaggregates are particles that can deleteriously cause false positive results during drug screening efforts, but alternatively, they may be used to improve pharmacokinetics when developed for drug delivery purposes. The structural features of molecules that drive nanoaggregate formation remain elusive, however, and the prediction of intracellular aggregation and rational design of nanoaggregate-based carriers are still challenging. We investigate nanoaggregate self-assembly mechanisms using small molecule fragments to identify the critical molecular forces that contribute to self-assembly. We find that aromatic groups and hydrogen bond acceptors/donors are essential for nanoaggregate formation, suggesting that both π-π stacking and hydrogen bonding are drivers of nanoaggregation. We apply structure-assembly-relationship analysis to the drug sorafenib and discover that nanoaggregate formation can be predicted entirely using drug fragment substructures. We also find that drug nanoaggregates are stabilized in an amorphous core-shell structure. These findings demonstrate that rational design can address intracellular aggregation and pharmacologic/delivery challenges in conventional and fragment-based drug development processes.

On-DNA hydroalkylation of N-vinyl heterocycles via photoinduced EDA-complex activation

The emergence of DNA-encoded library (DEL) technology has provided a considerable advantage to the pharmaceutical industry in the pursuit of discovering novel therapeutic candidates for their drug development initiatives. This combinatorial technique not only offers a more economical, spatially efficient, and time-saving alternative to the existing ligand discovery methods, but also enables the exploration of additional chemical space by utilizing novel DNA-compatible synthetic transformations to leverage multifunctional building blocks from readily available substructures. In this report, a decarboxylative-based hydroalkylation of DNA-conjugated N-vinyl heterocycles enabled by single-electron transfer (SET) and subsequent hydrogen atom transfer through electron-donor/electron-acceptor (EDA) complex activation is detailed. The simplicity and robustness of this method permits inclusion of a broad array of alkyl radical precursors and DNA-tethered nitrogenous heterocyles to generate medicinally relevant substituted heterocycles with pendant functional groups. Moreover, a successful telescoped route provides the opportunity to access a broad range of intricate structural scaffolds by employing basic carboxylic acid feedstocks.

Enabling synthesis in fragment-based drug discovery (FBDD): microscale high-throughput optimisation of the medicinal chemist's toolbox reactions

Miniaturised high-throughput experimentation (HTE) is widely employed in industrial and academic laboratories for rapid reaction optimisation using material-limited, multifactorial reaction condition screening. In fragment-based drug discovery (FBDD), common toolbox reactions such as the Suzuki-Miyaura and Buchwald-Hartwig cross couplings can be hampered by the fragment's intrinsic heteroatom-rich pharmacophore which is required for ligand-protein binding. At Astex, we are using microscale HTE to speed up reaction optimisation and prevent target down-prioritisation. By identifying catalyst/base/solvent combinations which tolerate unprotected heteroatoms we can rapidly optimise key cross-couplings and expedite route design by avoiding superfluous protecting group manipulations. However, HTE requires extensive upfront training, and this modern automated synthesis technique largely differs to the way organic chemists are traditionally trained. To make HTE accessible to all our synthetic chemists we have developed a semi-automated workflow enabled by pre-made 96-well screening kits, rapid analytical methods and in-house software development, which is empowering chemists at Astex to run HTE screens independently with minimal training.

QM assisted ML for 19F NMR chemical shift prediction

BACKGROUND

Ligand-observed 19F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens.

METHODS

In a previous publication, we introduced a method to predict 19F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set.

RESULTS

Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm .

Hot-Spot Residue-Based Virtual Screening of Novel Selective Estrogen-Receptor Degraders for Breast Cancer Treatment

The estrogen-receptor alfa (ERα) is considered pivotal for breast cancer treatment. Although selective estrogen-receptor degraders (SERDs) have been developed to induce ERα degradation and antagonism, their agonistic effect on the uterine tissue and poor pharmacokinetic properties limit further application of ERα; thus, discovering novel SERDs is necessary. The ligand preferentially interacts with several key residues of the protein (defined as hot-spot residues). Improving the interaction with hot-spot residues of ERα offers a promising avenue for obtaining novel SERDs. In this study, pharmacophore modeling, molecular mechanics/generalized Born surface area (MM/GBSA), and amino-acid mutation were combined to determine several hot-spot residues. Focusing on the interaction with these hot-spot residues, hit fragments A1-A3 and A9 were virtually screened from two fragment libraries. Finally, these hit fragments were linked to generate compounds B1-B3, and their biological activities were evaluated. Remarkably, compound B1 exhibited potent antitumor activity against MCF-7 cells (IC50 = 4.21 nM), favorable ERα binding affinity (Ki = 14.6 nM), and excellent ERα degradative ability (DC50 = 9.7 nM), which indicated its potential to evolve as a promising SERD for breast cancer treatment.

Fragment-based and structure-guided discovery of perforin inhibitors

Perforin is a pore-forming protein whose normal function enables cytotoxic T and natural killer (NK) cells to kill virus-infected and transformed cells. Conversely, unwanted perforin activity can also result in auto-immune attack, graft rejection and aberrant responses to pathogens. Perforin is critical for the function of the granule exocytosis cell death pathway and is therefore a target for drug development. In this study, by screening a fragment library using NMR and surface plasmon resonance, we identified 4,4-diaminodiphenyl sulfone (dapsone) as a perforin ligand. We also found that dapsone has modest (mM) inhibitory activity of perforin lytic activity in a red blood cell lysis assay in vitro. Sequential modification of this lead fragment, guided by structural knowledge of the ligand binding site and binding pose, and supported by SPR and ligand-detected 19F NMR, enabled the design of nanomolar inhibitors of the cytolytic activity of intact NK cells against various tumour cell targets. Interestingly, the ligands we developed were largely inert with respect to direct perforin-mediated red blood cell lysis but were very potent in the context of perforin's action on delivering granzymes in the immune synapse, the context in which it functions physiologically. Our work indicates that a fragment-based, structure-guided drug discovery strategy can be used to identify novel ligands that bind perforin. Moreover, these molecules have superior physicochemical properties and solubility compared to previous generations of perforin ligands.

Discovery of a fragment hit compound targeting D-Ala:D-Ala ligase of bacterial peptidoglycan biosynthesis

Bacterial resistance is an increasing threat to healthcare systems, highlighting the need for discovering new antibacterial agents. An established technique, fragment-based drug discovery, was used to target a bacterial enzyme Ddl involved in the biosynthesis of peptidoglycan. We assembled general and focused fragment libraries that were screened in a biochemical inhibition assay. Screening revealed a new fragment-hit inhibitor of DdlB with a Ki value of 20.7 ± 4.5 µM. Binding to the enzyme was confirmed by an orthogonal biophysical method, surface plasmon resonance, making the hit a promising starting point for fragment development.

A data-driven machine learning approach for discovering potent LasR inhibitors

The rampant spread of multidrug-resistant Pseudomonas aeruginosa strains severely threatens global health. This severity is compounded against the backdrop of a stagnating antibiotics development pipeline. Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the las quorum sensing (QS) system remains an attractive therapeutic strategy to combat P. aeruginosa infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. In this work, we demonstrated using a Multilayer Perceptron (MLP) algorithm boosted with AdaBoostM1 to discriminate between active and inactive LasR inhibitors. The optimal model performance was evaluated using 5-fold cross-validation and test sets. Our best model achieved a 90.7% accuracy in distinguishing active from inactive LasR inhibitors, an area under the Receiver Operating Characteristic Curve value of 0.95, and a Matthews correlation coefficient value of 0.81 when evaluated using test sets. Subsequently, we deployed the model against the Enamine database. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies, Molecular Dynamics simulations, MM-GBSA analysis, and Free Energy Landscape analysis. Our data indicate that several of our chosen top hits showed better ligand-binding affinities than naringenin, a competitive LasR inhibitor. Among the six top hits, five of these compounds were predicted to be LasR inhibitors that could be used to treat P. aeruginosa-associated infections. To our knowledge, this study provides the first assessment of using an MLP-based QSAR model for discovering potent LasR inhibitors to attenuate P. aeruginosa infections.

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.