Fragment-based Drug Discovery Research Articles

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase.

The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV-vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4H-pyrido[1,2-a]pyrimidin-4-one.

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.

Practical Perspectives and Roles of XRD in Drug Discovery: A Review

Abstract: Biophysical techniques include various methodologies applied in studying biological systems at the molecular and cellular level for the drug discovery process. Various methods like isothermal calorimetry, electron microscopy, XRD (X-ray diffraction), electron microscopy, mass spectrometry, atomic force microscopy, differential scanning calorimetry, surface plasmon resonance, and nuclear magnetic resonance are important techniques for drug discovery. Out of these techniques, XRD is widely employed in structure-based drug discovery, whereas FBDD (fragment-based drug discovery) is widely used in the different phases of drug discovery. XRD was considered one of the most important tools for structure determination of biomolecules and peptides. Consistent development and advancement in XRD improved the various aspects of data processing, collection, sample loading, and increased throughput. This advancement is crucial in obtaining highly resolved protein and other biomolecule crystal structures. The structure obtained from XRD forms the core of structure-based drug discovery and FBDD. This review article focuses on the different roles of biophysical techniques with special emphasis on advancement, data collection, and XRD's role in different drug discovery phases.

Fragment-based drug discovery and biological evaluation of novel cannabinol-based inhibitors of oxytosis/ferroptosis for neurological disorders

The oxytosis/ferroptosis regulated cell death pathway is an emerging field of research owing to its pathophysiological relevance to a wide range of neurological disorders, including Alzheimer's and Parkinson's diseases and traumatic brain injury. Developing novel neurotherapeutics to inhibit oxytosis/ferroptosis offers exciting opportunities for the treatment of these and other neurological diseases. Previously, we discovered cannabinol (CBN) as a unique, potent inhibitor of oxytosis/ferroptosis by targeting mitochondria and modulating their function in neuronal cells. To further elucidate which key pharmacophores and chemical space are essential to the beneficial effects of CBN, we herein introduce a fragment-based drug discovery strategy in conjunction with cell-based phenotypic screens using oxytosis/ferroptosis to determine the structure-activity relationship of CBN. The resulting information led to the development of four new CBN analogs, CP1-CP4, that not only preserve the sub-micromolar potency of neuroprotection and mitochondria-modulating activities seen with CBN in neuronal cell models but also have better druglike properties. Moreover, compared to CBN, the analog CP1 shows improved in vivo efficacy in the Drosophila model of mild traumatic brain injury. Together these studies identify the key molecular scaffolds of cannabinoids that contribute to neuroprotection against oxytosis/ferroptosis. They also highlight the advantageous approach of combining in vitro cell-based assays and rapid in vivo studies using Drosophila models for evaluating new therapeutic compounds.

Fragment-based Drug Discovery Strategy and its Application to the Design of SARS-CoV-2 Main Protease Inhibitor.

Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) emerged at the end of 2019, causing a highly infectious and pathogenic disease known as 2019 coronavirus disease. This disease poses a serious threat to human health and public safety. The SARS-CoV-2 main protease (Mpro) is a highly sought-after target for developing drugs against COVID-19 due to its exceptional specificity. Its crystal structure has been extensively documented. Numerous strategies have been employed in the investigation of Mpro inhibitors. This paper is primarily concerned with Fragment-based Drug Discovery (FBDD), which has emerged as an effective approach to drug design in recent times. Here, we summarize the research on the approach of FBDD and its application in developing inhibitors for SARS-CoV-2 Mpro.

Abstract 3324: Discovery of a novel SOS1 inhibitor, AIG01012, targeting pan-KRAS mutant cancers

Abstract Targeting the SOS1-KRAS interaction represents a pivotal strategy in mitigating aberrant KRAS signaling, which is implicated in numerous malignancies. At AIGEN Sciences, Inc., we have harnessed an advanced human-in-the-loop (HITL) AI-based drug discovery platform (AIGEN ChemTailor). This innovative platform allows online learning through expert feedback, optimizing our drug development endeavors. We designed a potent SOS1 inhibitor, AIG01012 using AIGEN ChemTailor in combination with fragment-based drug discovery (FBDD) and molecular modeling techniques. We conceived numerous compounds and shortlisted the leading candidates for extensive testing after meticulous computer simulations. AIG01012 manifested substantial inhibitory effects against several KRAS mutant interactions with SOS1, specifically SOS1-KRAS WT, SOS1-KRAS G12C, SOS1-KRAS G12D, SOS1-KRAS G12V, and SOS1-KRAS G13D. However, its inhibitory capacity did not extend to the SOS2-KRAS G12C interaction. AIG01012 demonstrated remarkable inhibitory effects in 3D cell proliferation assays across various cell lines, including H358, MIA-PaCa2, SW837, AsPC-1, A427, SW620, Capan-2, A549, DLD-1, MKN-1, and H1666. Moreover, AIG01012 showed synergistic antiproliferative effects when combined with approved drugs such as sotorasib (for lung cancer with KRAS mutations), trametinib (a MEK inhibitor), and Osimertinib (targets specific EGFR mutations) in either H358 or PC-9 cell lines. Mouse studies further confirmed its favorable pharmacological properties, and its efficacy was evaluated in subsequent mouse tumor model. In summary, utilizing our human-in-the-loop (HITL) AI platform, AIGEN ChemTailor, we have efficiently derived AIG01012 as a potent SOS1 inhibitor that targets a wide range of KRAS interactions. With its notable in vitro and in vivo outcomes, AIG01012 is set for further exploration and prospective clinical trials as a significant contender in KRAS-targeted cancer treatments. Citation Format: Jihye Kim, Sejeong Park, Sunkyu Kim, Sanghoon Lee, Kiwoong Yoo, Ho-Jeong Lee, Jaewoo Kang, Kwang-Ok Lee. Discovery of a novel SOS1 inhibitor, AIG01012, targeting pan-KRAS mutant cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3324.

Abstract 5949: The clinical dual KRASG12C inhibitor FMC-376 has demonstrated potential as both a monotherapy and in combination for the treatment of patients with KRASG12C mutation positive NSCLC

Abstract KRAS, a membrane-associated guanosine triphosphatase (GTPase), is the most frequently mutated oncogene in cancer with activating mutations occurring in approximately 25% of NSCLC. Of these, KRASG12C occurs in approximately 14% of adenocarcinomas and in 0.5 to 4% of squamous NSCLCs. This mutation impairs GTPase activity and GTP-hydrolysis leading to an increase in the active, GTP-bound (ON), state. KRAS was long considered undruggable until the recent discovery of inhibitors that bind the GDP-bound, inactive (OFF) state of KRASG12C. The most clinically advanced of these first-generation molecules have demonstrated clinical response rates of 37-43% and 6 to 7-month progression-free survival in patients with KRASG12C positive NSCLC. While significant, a majority of cancers do not respond, and acquired resistance is common. FMC-376 is a next-generation dual inhibitor of both the ON and OFF states of KRASG12Cwith the potential to deliver improved outcomes as both a monotherapy and in combination, for patients with KRASG12C positive NSCLC. FMC-376 was discovered through the FrontierTM platform, which integrates chemoproteomics, machine-learning, and covalent fragment-based drug discovery. The potential for FMC-376 in the treatment of patients with KRASG12C mutant NSCLC has been assessed in a broad panel of NSCLC PDX models representing settings of sotorasib/adagrasib resistance, in a model of NSCLC CNS-metastasis and in combination with an immune checkpoint inhibitor. The dual ON + OFF state mechanism of action of FMC-376 is highly active across a panel of NSCLC CDX and PDX models, including those that carry co-mutations that are associated with both primary and acquired resistance to the OFF-state inhibitors adagrasib and sotorasib in NSCLC. These drivers of resistance include activating mutations of receptor tyrosine kinases (e.g. EGFR, MET, etc.), co-mutations including KEAP1 and adaptive resistance due to release of negative feedback control. Evaluation of FMC-376 in models where EGFR signaling is induced by EGF demonstrated rapid and durable target engagement in contrast to both sotorasib and adagrasib which show decreased effectiveness after EGF stimulation. As a result, FMC-376 is highly effective in NSCLC PDX models that have EGFR and MET upregulation. In addition, 27-42% of patients with KRASG12C positive NSCLC present with CNS metastasis. FMC-376 is highly active in an intracranial NSCLC model of CNS-metastasis, resulting in tumor regression. Furthermore, FMC-376 has demonstrated activity in combination with an anti-PD-1 mAb in a KRASG12C mutant mouse syngeneic tumor model. Together, these data demonstrate the potential of FMC-376, a next-generation clinical stage dual inhibitor of ON and OFF state KRASG12C, for the treatment of patients with KRASG12C mutant NSCLC as well as other KRASG12C positive tumors. Citation Format: Yan Wang, Allison Roberts, Philamer Calses, Richard M. Neve, Jocelyn Staunton, Kevin R. Webster. The clinical dual KRASG12C inhibitor FMC-376 has demonstrated potential as both a monotherapy and in combination for the treatment of patients with KRASG12C mutation positive NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5949.

Abstract 7148: Small molecule inhibitor of FEN1 nuclease utilizing a novel metal-binding pharmacophore synergizes with inhibitors of USP1, PARP, PARG and ATR

Abstract Flap endonuclease 1 (FEN1) is a structure-specific metallonuclease that cleaves 5’ DNA flaps during replication and repair. FEN1 is an attractive target for development of anticancer therapeutics because it is overexpressed in many tumor types and has a large number of synthetic lethality partners including genes in Homologous Recombination (HR) pathway (Mengwasser et al., 2019; Guo et al., 2020). Utilizing fragment-based drug discovery approach we identified a novel metal-binding pharmacophore that binds to the two magnesium ions in the FEN1 active site. Further elaboration using fragment growth strategies resulted in highly potent and selective inhibitors. The current lead (BSM-1516) is ~65-fold more potent against FEN1 than its related enzyme Exonuclease 1 (Exo1) in biochemical assays (IC50 of 7 nM and 460 nM, respectively), an improvement of more than an order of magnitude in selectivity compared to earlier efforts. FEN1 target engagement in live cells was validated by cellular thermal shift assay (CETSA EC50 of 24 nM). Inhibition of FEN1 led to its increased association with chromatin in S-phase cells and recruitment of PARP1 enzyme. In clonogenic assay, BRCA2-deficient DLD1 cells were ~15-fold more sensitive to FEN1 inhibition than their isogenic BRCA2-wild-type counterparts (EC50 of 350 nM and 5 µM, respectively), confirming the increased susceptibility of HR deficient cancer cells to FEN1 inhibition. Treatment of BRCA2-deficient but not wild-type DLD1 cells with BSM-1516 resulted in cell cycle arrest and dose-dependent decrease of S-phase BrdU incorporation into DNA. Cell cycle arrest of BRCA2-deficient DLD1 cells was accompanied by DNA damage signaling and by accumulation of chromatin-bound RPA32, a marker of ssDNA. To explore synthetic lethal interactions of FEN1 with other DNA repair genes we performed FEN1-inhibitor-anchored CRISPR screen. This analysis revealed that in addition to HR pathway inactivation, genomic perturbations in EXO1, USP1 and PARP1 genes sensitized cells to FEN1 inhibition. Synergistic relationships of BSM-1516 and its combination potential were further explored in viability studies with a panel of DDR inhibitors (n=25) in BRCA2-proficient and deficient cell lines. Strong synergy was identified with multiple drug classes that included inhibitors of USP1 (KSQ-4279), PARP (Olaparib, Niraparib, Talazoparib, AZD5305), PARG (PDD 00017273) and ATR (AZD6738, VE-822, Elimusertib). In vitro ADME assays and in vivo PK studies showed that BSM-1516 has properties suitable for in vivo testing, either as a single agent or in combination with synergistic DDR inhibitors, an investigation that is currently underway. Citation Format: Jason Munguia, Sanjay Agarwalla, Elaine Guo, Dave Martin, Junhua Fan, Dave Lonergan, David Puerta, Zachary Zimmerman, Konstantin Taganov. Small molecule inhibitor of FEN1 nuclease utilizing a novel metal-binding pharmacophore synergizes with inhibitors of USP1, PARP, PARG and ATR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7148.

Abstract 5948: FMC-376 a dual inhibitor of ON and OFF states of KRASG12C is broadly active in PDX models of resistance

Abstract KRASG12C is a frequent activating mutation occurring in NSCLC, CRC, PDAC and other tumors that shifts KRAS into the active, GTP-bound (ON) state. KRAS was long considered undruggable until the recent discovery of single acting inhibitors, including those that bind the GDP-bound, inactive (OFF), state of KRASG12C. The most clinically advanced of these first-generation molecules have demonstrated clinical response rates of 37-43% and 6 to 7-month progression-free survival in lung cancer patients. While an important advance, a majority of cancers do not respond, and acquired resistance is common. FMC-376 is a next-generation dual inhibitor of both the ON and OFF states of KRASG12C with the potential to overcome resistance and deliver best-in-class durability of response. FMC-376 was discovered through the application of the FrontierTM platform, which integrates chemoproteomics, machine-learning, and covalent fragment-based drug discovery. FMC-376 is a highly selective, irreversible inhibitor of KRASG12C that covalently binds to the mutant cysteine in KRASG12C and directly blocks the ON and OFF states of the mutant KRAS protein leading to a complete, durable blockade of downstream signaling. This further translates into more durable tumor regressions in vivo in comparison to OFF state inhibitors like sotorasib. Clinical resistance to first-generation inhibitors that can only bind the OFF state of KRASG12C has been associated with multiple mechanisms that drive upregulation of ON state KRASG12C. These known drivers of resistance include activating co-mutation/amplification/fusions of receptor tyrosine kinases, amplification of KRASG12C and non-mutational mechanisms that lead to paradoxical activation of KRASG12C due to release of negative feedback control upon KRAS inhibition, often referred to as adaptive resistance. In addition, multiple mutations that may act independently of KRAS are also associated with resistance. These include mutation of KEAP1, CDKN2A, SMARCA4, PI3K, PTEN, MTOR, HRAS, NRAS, RAF, and MAPK. In vivo profiling of FMC-376 against a panel KRASG12C mutant patient derived xenograft (PDX) models of NSCLC, CRC and PDAC, has demonstrated that dual inhibition of both ON and OFF KRASG12C leads to complete suppression and regression of tumors carrying a broad spectrum of known genomic and non-genomic (adaptive) drivers of clinical resistance to OFF state KRASG12C inhibitors. FMC-376 a clinical stage dual inhibitor of ON and OFF state KRASG12C has the potential to overcome limitations of single acting and OFF state KRASG12C inhibitors. Citation Format: Yan Wang, Richard M. Neve, Jocelyn Staunton, Kevin R. Webster. FMC-376 a dual inhibitor of ON and OFF states of KRASG12C is broadly active in PDX models of resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5948.

Chemical Space Profiling of SARS-CoV-2 PLpro Using DNA-Encoded Focused Libraries.

DNA-encoded library (DEL) technology is gaining attention for its rapid construction and deconvolution capabilities. Our study explored a novel strategy using rational DELs tailored for the SARS-CoV-2 papain-like protease, which revealed new fragments. Structural changes post-DEL screening mimic traditional medicinal chemistry lead optimization. We unveiled unique aromatic structures offering an alternative optimization path. Notably, we identified superior binding fragments targeting the BL2 groove. Derivative 16 emerged as the most promising by exhibiting IC50 values of 0.25 μM. Derivative 6, which features an aromatic fragment capped with a naphthalene moiety, showed IC50 values of 2.91 μM. Molecular modeling revealed hydrogen bond interactions with Lys157 residue and potential covalent interactions with nearby amino acid residues. This research underscored DEL's potential for fragment-based drug discovery against SARS-CoV-2 protease.

Synthesis and Chemoinformatic Analysis of Fluorinated Piperidines as 3D Fragments for Fragment-Based Drug Discovery.

The concise synthesis of a small library of fluorinated piperidines from readily available dihydropyridinone derivatives has been described. The effect of the fluorination on different positions has then been evaluated by chemoinformatic tools. In particular, the compounds' pKa's have been calculated, revealing that the fluorine atoms notably lowered their basicity, which is correlated to the affinity for hERG channels resulting in cardiac toxicity. The "lead-likeness" and three-dimensionality have also been evaluated to assess their ability as useful fragments for drug design. A random screening on a panel of representative proteolytic enzymes was then carried out and revealed that one scaffold is recognized by the catalytic pocket of 3CLPro (main protease of SARS-CoV-2 coronavirus).

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.

Recent Advances in Enzyme Inhibition: A Pharmacological Review

Abstract: Enzyme inhibition is a crucial mechanism for regulating biological processes and developing therapeutic interventions. This pharmacological review summarizes recent advances in enzyme inhibition, focusing on key developments and their implications for drug discovery and therapeutic strategies. It explains basic ideas, including the different kinds of inhibitors and how they work, and looks at recent advances in small-molecule inhibitor design, fragment-based drug discovery, and virtual screening techniques. The review also highlights the advances in targeting specific enzyme families, explaining the structural basis of enzyme-inhibitor interactions, optimizing inhibitor potency, selectivity, and pharmacokinetic properties, and new trends in enzyme inhibition. The clinical implications of recent advances in enzyme inhibition include the development of novel therapeutic agents for diseases like cancer, infectious diseases, and neurological disorders. The review addresses challenges and future directions in the field, such as optimizing drug safety, resistance mechanisms, and personalized medicine approaches. Overall, the insights provided in this review may inspire further research and collaborations to accelerate the translation of enzyme inhibitors into effective clinical treatments.

Review for QSAR studies and drug design of selected heterocyclic nucleus of antitubercular drugs

This review article explores the pressing issue of drug-resistant strains of Mycobacterium tuberculosis, which have emerged due to the widespread and uncontrolled use of antibiotics in clinical settings over several decades. In response to this challenge, various methods have been developed for synthesizing new antitubercular compounds. Among these, the fragment-based drug discovery (FBDD) approach has shown promise as an effective strategy. One class of compounds that has exhibited significant potential in combating tuberculosis is 1,2-diazoles. The article discusses the importance of these compounds and their potential as future antitubercular drugs. Additionally, it delves into the various strategies employed in drug development, emphasizing the relevance and efficacy of FBDD. Analytical methods play a crucial role in characterizing antitubercular antibiotics, and the article highlights liquid chromatography and voltammetry as preferred techniques for determining these compounds. The redox (oxidation/reduction) properties of antituberculars make them amenable to analysis using electrochemical methods, with voltammetry being particularly suitable. Furthermore, the article underscores the significance of utilizing voltammetry for quantifying different categories of antibiotics in both dosage forms and human body fluids. The affordability of this method makes it particularly advantageous for developing countries, providing a cost-effective approach to monitor and assess the presence of antitubercular drugs. Overall, the review article offers valuable insights into the current strategies for addressing drug resistance in tuberculosis and highlights the potential of 1,2-diazoles as future antitubercular agents.

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.

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.

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.

Fragment Merging, Growing, and Linking Identify New Trypanothione Reductase Inhibitors for Leishmaniasis.

Trypanothione reductase (TR) is a suitable target for drug discovery approaches against leishmaniasis, although the identification of potent inhibitors is still challenging. Herein, we harnessed a fragment-based drug discovery (FBDD) strategy to develop new TR inhibitors. Previous crystallographic screening identified fragments 1-3, which provided ideal starting points for a medicinal chemistry campaign. In silico investigations revealed critical hotspots in the TR binding site, guiding our structure- and ligand-based structure-actvity relationship (SAR) exploration that yielded fragment-derived compounds 4-14. A trend of improvement in Leishmania infantum TR inhibition was detected along the optimization and confirmed by the crystal structures of 9, 10, and 14 in complex with Trypanosoma brucei TR. Compound 10 showed the best TR inhibitory profile (Ki = 0.2 μM), whereas 9 was the best one in terms of in vitro and ex vivo activity. Although further fine-tuning is needed to improve selectivity, we demonstrated the potentiality of FBDD on a classic but difficult target for leishmaniasis.

Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece.

Fragment-based drug discovery (FBDD) has emerged as a powerful strategy to confront the challenges faced by conventional drug development approaches, particularly in the context of central nervous system (CNS) disorders. FBDD involves the screening of libraries that comprise thousands of small molecular fragments, each no greater than 300Da in size. Unlike the generally larger molecules from high-throughput screening that limit customisation, fragments offer a more strategic starting point. These fragments are inherently compact, providing a strong foundation with good binding affinity for the development of drug candidates. The minimal elaboration required to transition the hit into a drug-like molecule is not only accelerated, but also it allows for precise modifications to enhance both their activity and pharmacokinetic properties. This shift towards a fragment-centric approach has seen commercial success and holds considerable promise in the continued streamlining of the drug discovery and development process. In this review, we highlight how FBDD can be integrated into the CNS drug discovery process to enhance the exploration of a target. Furthermore, we provide recent examples where FBDD has been an integral component in CNS drug discovery programs, enabling the improvement of pharmacokinetic properties that have previously proven challenging. The FBDD optimisation process provides a systematic approach to explore this vast chemical space, facilitating the discovery and design of compounds piece by piece that are capable of modulating crucial CNS targets.

R-BRICS - A Revised BRICS Module That Breaks Ring Structures and Carbon Chains.

Molecular fragmentation has been frequently used for machine learning, molecular modeling, and drug discovery studies. However, the current molecular fragmentation tools often lead to large fragments that are useful to limited tasks. Specifically, long aliphatic chains, certain connected ring structures, fused rings, as well as various nitrogen-containing molecular entities often remain intact when using BRICS. With no known methods to solve this issue, we find that the fragments taken from BRICS are inflexible for tasks such as fragment-based machine learning, coarse-graining, and ligand-protein interaction assessment. In this work, a revised BRICS (r-BRICS) module is developed to allow more flexible fragmentation on a wider variety of molecules. It is shown that r-BRICS generates smaller fragments than BRICS, allowing localized fragment assessments. Furthermore, r-BRICS generates a fragment database with significantly more unique small fragments than BRICS, which is potentially useful for fragment-based drug discovery.