Efficient Exploration of Chemical Space with Docking and Deep Learning

Discovery of a potent PARP1 PROTAC as a chemosensitizer for the treatment of colorectal cancer

Given the vulnerability of colorectal cancer (CRC) patients could not obtain a sustained benefit from chemotherapy, combination therapy is frequently employed as a treatment strategy. Targeting PARP1 blockade exhibit specific toxicity towards tumor cells with BRCA1 or BRCA2 mutations through synthetic lethality. This study focuses on developing a series of potent PROTACs targeting PARP1 in order to enhance the sensitivity of CRC cells with BRCA1 or BRCA2 mutations to chemotherapy. Compound C6, obtained based on precise structural optimization of the linker, has been shown to effectively degrade PARP1 with a DC50 value of 58.14 nM. Furthermore, C6 significantly increased the cytotoxic efficacy of SN-38, an active metabolite of Irinotecan, in BRCA-mutated CRC cells, achieving a favorable combination index (CI) of 0.487. In conclusion, this research underscores the potential benefits of employing a combination therapy that utilizes PAPRP1 degrader C6 alongside Irinotecan for CRC patients harboring BRCA mutations in CRC.

CDK8 inhibitor KY-065 rescues skeletal abnormalities in achondroplasia model mice

Cyclin-dependent kinase 8 (CDK8) is a transcription-related CDK family member implicated in the regulation of bone homeostasis, and we recently demonstrated that our internally developed CDK8 inhibitor KY-065 can prevent postmenopausal osteoporosis in a mouse model. Achondroplasia (ACH), the most common form of genetic dwarfism in humans, is caused by a gain-of-function mutation in fibroblast growth factor receptor 3 (FGFR3), a receptor tyrosine kinase that activates downstream mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription (STAT) signaling pathways. The first precision drug approved for the treatment of ACH in children, the C-type natriuretic peptide analog vosoritide, antagonizes the MAPK pathway, while there are currently no effective and safe medications targeting the STAT1 pathway. Here, we demonstrate that KY-065 rescues impaired chondrogenesis and stunted long bone growth in the Fgfr3Ach mouse model of ACH. KY-065 inhibited CDK8 with high affinity in vitro by competing with ATP. The CDK8 expression and STAT1Ser727 phosphorylation were upregulated in chondrocytes isolated from ACH model mice, and KY-065 repressed its phosphorylation and restored normal chondrogenic differentiation without affecting MAPK activation. Moreover, daily administration of 10 mg/kg KY-065 to Fgfr3Ach mice (yielding a peak concentration of 22.0 ± 1.47 μM in plasma) resulted in significant elongation of long bone and improved growth plate cytoarchitecture. Collectively, these findings identify the CDK8 in chondrocytes as a potential therapeutic target for ACH and KY-065 as a promising candidate drug treatment for this debilitating skeletal disease.

Molecular Insights into Single-Chain Lipid Modulation of Acid-Sensing Ion Channel 3

Polyunsaturated fatty acids (PUFAs) and their analogs play a significant role in modulating the activity of diverse ion channels, and recent studies show that these lipids potentiate acid-sensing ion channels (ASICs), leading to increased activity. The potentiation of the channel stems from multiple gating changes, but the exact mechanism of these effects remains uncertain. We posit a mechanistic explanation for one of these changes in channel function, the increase in the maximal current, by applying a combination of electrophysiology and all-atom molecular dynamics simulations on open-state hASIC3. Microsecond-scale simulations were performed on open-state hASIC3 in the absence and presence of a PUFA, docosahexaenoic acid (DHA), and a PUFA analogue, N-arachidonyl glycine (AG). Intriguingly, our simulations in the absence of PUFA or PUFA analogs reveal that a tail from the membrane phospholipid POPC inserts itself into the pore of the channel through lateral fenestrations on the sides of the transmembrane segments, obstructing ion permeation through the channel. The binding of either DHA or AG prevented POPC from accessing the pore in our simulations, which relied on the block of ionic conduction by phospholipids. Finally, we use single-channel recording to show that DHA increases the amplitude of the single-channel currents in ASIC3, which is consistent with our hypothesis that PUFAs relieve the pore block of the channel induced by POPCs. Together, these findings offer a potential mechanistic explanation of how PUFAs modulate the ASIC maximal current, revealing a novel mechanism of action for PUFA-induced modulation of ion channels.

Ultra-Large Virtual Screening: Definition, Recent Advances, and Challenges in Drug Design

Virtual screening (VS) in drug design employs computational methodologies to systematically rank molecules from a virtual compound library based on predicted features related to their biological activities or chemical properties. The recent expansion in commercially accessible compound libraries and the advancements in artificial intelligence (AI) and computational power - including enhanced central processing units (CPUs), graphics processing units (GPUs), high-performance computing (HPC), and cloud computing - have significantly expanded our capacity to screen libraries containing over 109 molecules. Herein, we review the concept of ultra-large virtual screening (ULVS), focusing on the various algorithms and methodologies employed for virtual screening at this scale. In this context, we present the software utilized, applications, and results of different approaches, such as brute force docking, reaction-based docking approaches, machine learning (ML) strategies applied to docking or other VS methods, and similarity/pharmacophore search-based techniques. These examples represent a paradigm shift in the drug discovery process, demonstrating not only the feasibility of billion-scale compound screening but also their potential to identify hit candidates and increase the structural diversity of novel compounds with biological activities.

Molecular interactions between a diphenyl scaffold and PED/PEA15: Implications for type II diabetes therapeutics targeting PED/PEA15 - Phospholipase D1 interaction

In a recent study, we have identified BPH03 as a promising scaffold for the development of compounds aimed at modulating the interaction between PED/PEA15 (Phosphoprotein Enriched in Diabetes/Phosphoprotein Enriched in Astrocytes 15) and PLD1 (phospholipase D1), with potential applications in type II diabetes therapy. PED/PEA15 is known to be overexpressed in certain forms of diabetes, where it binds to PLD1, thereby reducing insulin-stimulated glucose transport. The inhibition of this interaction reestablishes basal glucose transport, indicating PED as a potential target of ligands capable to recover glucose tolerance and insulin sensitivity. In this study, we employ computational methods to provide a detailed description of BPH03 interaction with PED, evidencing the presence of a hidden druggable pocket within its PLD1 binding surface. We also elucidate the conformational changes that occur during PED interaction with BPH03. Moreover, we report new NMR data supporting the in-silico findings and indicating that BPH03 disrupts the PED/PLD1 interface displacing PLD1 from its interaction with PED. Our study represents a significant advancement toward the development of potential therapeutics for the treatment of type II diabetes.

Discovering a novel dual specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitor and its impact on tau phosphorylation and amyloid-β formation

Dual-specificity tyrosine-regulated kinase 1 A (DYRK1A) is crucial in neurogenesis, synaptogenesis, and neuronal functions. Its dysregulation is linked to neurodegenerative disorders like Down syndrome and Alzheimer's disease. Although the development of DYRK1A inhibitors has significantly advanced in recent years, the selectivity of these drugs remains a critical challenge, potentially impeding further progress. In this study, we utilised structure-based virtual screening (SBVS) from NCI library to discover novel DYRK1A inhibitors. The top-ranked compounds were then validated through enzymatic assays to assess their efficacy towards DYRK1A. Among them, NSC361563 emerged as a potent and selective DYRK1A inhibitor. It was shown to decrease tau phosphorylation at multiple sites, thereby enhancing tubulin stability. Moreover, NSC361563 diminished the formation of amyloid β and offered neuroprotective benefits against amyloid β-induced toxicity. Our research highlights the critical role of selective DYRK1A inhibitors in treating neurodegenerative diseases and presents a promising starting point for the development of targeted therapies.

Synthesis and in vitro exploration of the 8-carbo substituted 5-methoxyflavones as anti-breast and anti-lung cancer agents targeting protein kinases (VEGFR-2 & EGFR)

The 8-aryl-, 8-styryl- and 8-arylethynyl substituted 5-methoxyflavones were synthesized and characterized using a combination of spectroscopic techniques. Single crystal X-ray diffraction (XRD) study on a representative compound 3h shows an inverted dimer linked by fused ten and six-membered ring motifs involving intermolecular CO⋯HC and CH⋯OC hydrogen bonds. Compounds 3b, 3c, 3d, 4a and 4b exhibited strong activity against the human breast (MCF-7) cancer cell line (IC50 = 13.68 ± 0.72, 16.91 ± 0.40, 13.63 ± 0.36, 14.66 ± 0.47 and 12.26 ± 0.45 μM, respectively) and lung (A549) cancer cell line (IC50 = 15.38 ± 0.33, 10.00 ± 0.28, 12.38 ± 0.30, 12.84 ± 0.33 and 8.47 ± 0.30 μM, respectively) compared to quercetin (IC50 = 40.61 ± 1.07 and 58.17 ± 0.50 μM, respectively). Compounds 3b, 3c and 4b exhibited dual inhibitory effect against the vascular endothelial growth factor receptor-2 (VEGFR-2) and the epidermal growth factor receptor (EGFR) tyrosine kinase phosphorylation. Molecular docking revealed that strong alignment with the enzyme backbone is achieved mostly by hydrophobic (π-π, and π-H) contacts and by hydrogen bonding interaction with the residues in the active sites of VEGFR-2 and EGFR. The test compounds possess favorable drug-likeness properties, supporting their potential as promising therapeutic candidates.

Landscaping DNA binding potential of piperine derivatives by computational and biophysical methods

Piperine, the alkaloid from Black pepper, is known for its wide range of pharmacological effects. The DNA binding activity of piperine was reported earlier. In this work, we explore the DNA duplex binding properties of four piperine derivatives, piperonal, piperonyl alcohol, piperonylic acid, and piperic acid using biophysical and computational techniques. Various spectroscopic and calorimetric techniques were employed for the experimental analysis. We employed UV - vis absorption and fluorescence spectroscopy to verify the binding of piperine to calf thymus DNA (ctDNA). The energetics of this interaction were analysed using isothermal titration calorimetry (ITC). Conformational changes in DNA resulting from ligand interactions were investigated using circular dichroism spectroscopy. All these experimental results consistently demonstrate that the piperine derivatives exhibit stronger binding affinity to DNA than piperine. Computational analyses, utilizing molecular docking and dynamics, revealed similar results to experimental studies, indicating that the compounds bind to the minor groove of DNA like piperine. Both in vitro and in silico investigations demonstrated the strong DNA binding potential of the examined piperine derivatives. This study is the first to report on the comparative interaction between these piperine derivatives and a DNA duplex.

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.

Targeting Histidinol-Phosphate Aminotransferase in Acinetobacter baumannii with Salvianolic Acid B: A Structure-Based Approach to Novel Antibacterial Strategies

Undoubtedly, Acinetobacter baumannii is a major ESKAPE pathogen that poses a significant threat to public health, causing severe nosocomial infections with high mortality rates in healthcare settings. Due to the rapid development of antibiotic resistance, only a limited number of antibiotics remain effective against infections caused by multidrug-resistant (MDR) Acinetobacter baumannii. The discovery of new class of antibiotic molecules still lags behind the rate of growing worldwide burden of antimicrobial resistance (AMR). To expedite the discovery of new therapeutic molecules, we have focused on HisC from A. baumannii (AbHisC), a crucial enzyme involved in the seventh step of histidine biosynthesis. This pathway is absent in humans. We have employed the advanced computational techniques to target this promising drug target. AbHisC was cloned, overexpressed, and purified. Three distinct sets of libraries containing ∼60,000 natural compounds from ZINC database were screened against AbHisC using Schrödinger's glide module software. Based on the docking score and glide energy, top 25 hits were further subjected to induced fit (IF) docking. Top four out of the twenty five compounds from IF docking were subjected to 100ns molecular dynamics simulations, and it was observed that salvianolic acid B (SA-B) (a naturally occurring compound) complex with AbHisC, was found to be extremely stable. The glide energy and docking score of SA-B were -88.59 kcal/mol and -10.4 kcal/mol. SA-B was also found to quench the intrinsic fluorescence of tyrosine indicating its binding to the target. The dissociation constant calculated using Surface Plasmon Resonance was found to be 3.4x10-9M. Based on these results we can conclude that SA-B can serve as the potential inhibitor of AbHisC that can further form the basis of structure based drug design against this deadly pathogen.

vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening

The enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-finding discovery rates. Traditional virtual screening methods are often inaccurate, with most compounds nominated in a virtual screen not engaging the intended target protein to any detectable extent. Emerging machine learning approaches have made significant progress in this regard, including our previously described tool vScreenML. The broad adoption of vScreenML was hindered by its challenging usability and dependencies on certain obsolete or proprietary software packages. Here, we introduce vScreenML 2.0 to address each of these limitations with a streamlined Python implementation. Through careful benchmarks, we show that vScreenML 2.0 outperforms other widely used tools for virtual screening hit discovery.

Recent Applications of In Silico Approaches for Studying Receptor Mutations Associated with Human Pathologies

In recent years, the advent of computational techniques to predict the potential activity of a drug interacting with a receptor or to predict the structure of unidentified proteins with aberrant characteristics has significantly impacted the field of drug design. We provide a comprehensive review of the current state of in silico approaches and software for investigating the effects of receptor mutations associated with human diseases, focusing on both frequent and rare mutations. The reported techniques include virtual screening, homology modeling, threading, docking, and molecular dynamics. This review clearly shows that it is common for successful studies to integrate different techniques in drug design, with docking and molecular dynamics being the most frequently used techniques. This trend reflects the current emphasis on developing novel therapies for diseases resulting from receptor mutations with the recently discovered AlphaFold algorithm as the driving force.

Introducing SpaceGA: A Search Tool to Accelerate Large Virtual Screenings of Combinatorial Libraries

The growth of make-on-demand libraries in recent years has provided completely new possibilities for virtual screening for discovering new hit compounds with specific and favorable properties. However, since these libraries now contain billions of compounds, screening them using traditional methods such as molecular docking has become challenging and requires substantial computational resources. Thus, to take real advantage of the new possibilities introduced by the make-on-demand libraries, different methods have been proposed to accelerate the screening process and prioritize molecules for evaluation. Here, we introduce SpaceGA, a genetic algorithm that leverages the rapid similarity search tool SpaceLight (Bellmann, L.; Penner, P.; Rarey, M. Topological similarity search in large combinatorial fragment spaces. J. Chem. Inf. Model. 2021, 61, 238-251). to constrain the optimization process to accessible compounds within desired combinatorial libraries. As shown herein, SpaceGA is able to efficiently identify molecules with desired properties from trillions of synthesizable compounds by enumerating and evaluating only a small fraction of them. On this basis, SpaceGA represents a promising new tool for accelerating and simplifying virtual screens of ultralarge combinatorial databases.

Key Amino Acid Residues of the Agt1 Transporter for Trehalose Transport by Saccharomyces cerevisiae

Trehalose is crucial for the stress resistance of Saccharomyces cerevisiae, primarily through its stabilization of proteins and membranes. The Agt1 transporter, a member of the Major Facilitator Superfamily, mediates trehalose uptake, a key process for maintaining cellular integrity under stress. Despite its importance, the molecular mechanisms of Agt1-mediated trehalose transport remain underexplored. In this study, we expressed and purified the trehalase enzyme TreA from E. coli to develop reliable trehalose assays. We screened 257 wild S. cerevisiae isolates, identifying strains with enhanced trehalose transport capacities. Comparative analyses, including structural modeling and molecular docking, revealed that specific Agt1 variants exhibited significantly higher transport efficiency, influenced by key residues in the transporter. Molecular dynamics simulations and steered molecular dynamics provided further insights, particularly into the role of the Agt1 channel head region in substrate recognition and binding. Site-directed mutagenesis validated these findings, showing that mutations at critical residues, such as 156Q, 164L, 256Q, 395E, 396R, and 507Y significantly reduced transport activity, while 137Q, 230T, and 514 N increased efficiency under certain conditions.

Investigating the effect of 1,2-Dibenzoylhydrazine on Staphylococcus aureus using integrated computational approaches

Staphylococcus aureus, a notorious member of the ESKAPE pathogens, poses significant public health challenges due to its virulence and multidrug-resistant nature, particularly in methicillin-resistant S. aureus (MRSA) strains. With the increasing threat of antibiotic resistance, there is an urgent need to develop novel antibiotic agents. This study therefore aims to explore the antibacterial potential of 1,2-dibenzoylhydrazine (DBH) as a scaffold against S. aureus drug target enzymes, using integrated computational approaches. The study utilized molecular docking, lead optimization, and structure-based virtual screening techniques to evaluate the binding affinities of DBH and its derivatives against various S. aureus enzymes. Prime/MM-GBSA calculations were performed to validate the binding affinities obtained, and molecular dynamics (MD) simulations were conducted to assess the stability of the DBHs-enzyme complexes. Results indicated that, out of twenty enzymes from S. aureus examined against DBH, carotenoid dehydrosqualene synthase was predicted as a suitable target enzyme for DBH, showing a binding affinity of -8.027 kcal/mol. A lead optimization operation of the compound generated 27 DBH derivatives out of which four exhibited enhanced binding affinities compared to both DBH and a standard antibiotic, ofloxacin. The QSAR model predicted that, DBH and molecule_D_1 have higher PIC50 of 4.779 µM compared with the standard drug (ofloxacin = 4.678 µM). MD simulations confirmed the stability of the top-scoring derivatives within the enzyme's binding pocket, with RMSD and RMSF analyses supporting their potential as inhibitors of the enzyme. In conclusion, this study has predicted the effect of DBH derivatives on S. aureus based on their in silico inhibitory capacity against the carotenoid dehydrosqualene synthase from the organism. Future work will seek to experimentally validate these findings against the suggested enzyme.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00278-1.

Navigating Ultralarge Virtual Chemical Spaces with Product-of-Experts Chemical Language Models

Ultralarge virtual chemical spaces have emerged as a valuable resource for drug discovery, providing access to billions of make-on-demand compounds with high synthetic success rates. Chemical language models can potentially accelerate the exploration of these vast spaces through direct compound generation. However, existing models are not designed to navigate specific virtual chemical spaces and often overlook synthetic accessibility. To address this gap, we introduce product-of-experts (PoE) chemical language models, a modular and scalable approach to navigating ultralarge virtual chemical spaces. This method allows for controlled compound generation within a desired chemical space by combining a prior model pretrained on the target space with expert and anti-expert models fine-tuned using external property-specific data sets. We demonstrate that the PoE chemical language model can generate compounds with desirable properties, such as those that favorably dock to dopamine receptor D2 (DRD2) and are predicted to cross the blood-brain barrier (BBB), while ensuring that the majority of generated compounds are present within the target chemical space. Our results highlight the potential of chemical language models for navigating ultralarge virtual chemical spaces, and we anticipate that this study will motivate further research in this direction. The source code and data are freely available at https://github.com/shuyana/poeclm.

Unraveling the molecular mechanism of FgGcn5 inhibition by phenazine-1-carboxamide: combined in silico and in vitro studies

BACKGROUND

Fusarium head blight (FHB), mainly caused by Fusarium graminearum (F. graminearum), remains a devastating disease worldwide. The histone acetyltransferase Gcn5 plays a crucial role in epigenetic regulation. Aberrant Gcn5 acetylation activity can result in serious impacts such as impaired growth and development in organisms. The secondary metabolite phenazine-1-carboxamide (PCN) inhibits F. graminearum by blocking the acetylation process of Gcn5 (FgGcn5), and is currently used to control FHB. However, the molecular basis of acetylation inhibition by PCN remains to be further explored.

RESULTS

Our molecular dynamics simulations revealed that PCN binds to the cleft in FgGcn5 where histone H3 is bound, with key amino acid residues including Leu96 (L96), Arg121 (R121), Phe133 (F133), Tyr169 (Y169), and Tyr201 (Y201), preventing FgGcn5 from binding to histone H3 and affecting histone H3 from being acetylated. Experimental validation of key amino acid mutations further confirmed the impact of these mutations on the interaction of FgGcn5 with PCN and histone H3 peptide.

CONCLUSION

In summary, our study sheds light on the mechanism by which PCN inhibits the acetylation function of FgGcn5, providing a foundation for the development of drugs or fungicides targeting histone acetyltransferases. © 2024 Society of Chemical Industry.

Scaffold Hopping Method for Design and Development of Potential Allosteric AKT Inhibitors

Targeting AKT is a practical strategy for cancer therapy in many cancer types. Targeted inhibitors of AKT are attractive solutions for inhibiting the interconnected signaling pathways, like PI3K/Akt/mTOR. Allosteric inhibitors are more desirable among different classes of AKT inhibitors as they could be more specific with fewer off-target proteins. In this study, a ligand/structure-based pipeline was developed to design new allosteric AKT inhibitors by employing the core hopping method. Triciribine, a traditional allosteric AKT inhibitor was used as the template, and the FDA-approved kinase inhibitors for cancer treatment were considered as the cores. The allosteric site in the crystal structure of AKT1 was used to screen the designed compounds. The results were further evaluated using molecular docking, ADME/T analysis, molecular dynamics (MD) simulation, and binding free energy calculations. The outcomes introduced 24 newly designed inhibitors, amongst which three compounds C6, C20, and C16 showed remarkable binding affinity to AKT1. While the docking scores for triciribine was around - 8.6 kcal/mol, the docking scores of these compounds were about - 11 to - 13 kcal/mol. The MD results indicated that designed compounds target the essential residues of the PH domain and kinase domain of AKT, such as Trp80, Thr211, Tyr272, Asp274, and Asp292. Scaffold hopping is a tremendous tool for designing novel anti-cancer agents by improving already known and potential drug compounds. The designed compounds are worth to be examined by experimental investigation in vitro and in vivo.

Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds

Make-on-demand chemical libraries have drastically increased the reach of molecular docking, with the enumerated ready-to-dock ZINC-22 library approaching 6.4 billion molecules (July 2024). While ever-growing libraries result in better-scoring molecules, the computational resources required to dock all of ZINC-22 make this endeavor infeasible for most. Here, we organize and traverse chemical space with hierarchical navigable small-world graphs, a method we term retrieval augmented docking (RAD). RAD recovers most virtual actives, despite docking only a fraction of the library. Furthermore, RAD is protein-agnostic, supporting additional docking campaigns without additional computational overhead. In depth, we assess RAD on published large-scale docking campaigns against D4 and AmpC spanning 99.5 million and 138 million molecules, respectively. RAD recovers 95% of DOCK virtual actives for both targets after evaluating only 10% of the libraries. In breadth, RAD shows widespread applicability against 43 DUDE-Z proteins, evaluating 50.3 million associations. On average, RAD recovers 87% of virtual actives while docking 10% of the library without sacrificing chemical diversity.

Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery

Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.

Unveiling the Anticancer Potential: Computational Exploration of Nitrogenated Derivatives of (+)-Pancratistatin as Topoisomerase I Inhibitors

Cancer poses a substantial global health challenge, driving the need for innovative therapeutic solutions that offer improved effectiveness and fewer side effects. Topoisomerase I (Topo I) has emerged as a validated molecular target in the pursuit of developing anticancer drugs due to its critical role in DNA replication and transcription. (+)-Pancratistatin (PST), a naturally occurring compound found in various Amaryllidaceae plants, exhibits promising anticancer properties by inhibiting Topo I activity. However, its clinical utility is hindered by issues related to limited chemical availability and aqueous solubility. To address these challenges, molecular modelling techniques, including virtual screening, molecular docking, molecular mechanics with generalised born and surface area solvation (MM-GBSA) calculations, and molecular dynamics simulations were utilised to evaluate the binding interactions and energetics of PST analogues with Topo I, comparing them with the well-known Topo I inhibitor, Camptothecin. Among the compounds screened for this study, nitrogenated analogues emerged as the most encouraging drug candidates, exhibiting improved binding affinities, favourable interactions with the active site of Topo I, and stability of the protein-ligand complex. Structural analysis pinpointed key molecular determinants responsible for the heightened potency of nitrogenated analogues, shedding light on essential structural modifications for increased activity. Moreover, in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions highlighted favourable drug-like properties and reduced toxicity profiles for the most prominent nitrogenated analogues, further supporting their potential as effective anticancer agents. In summary, this screening study underscores the significance of nitrogenation in augmenting the anticancer efficacy of PST analogues targeting Topo I. The identified lead compounds exhibit significant potential for subsequent experimental validation and optimisation, thus facilitating the development of novel and efficacious anticancer therapeutics with enhanced pharmacological profiles.

In Silico Screening of 1,3,4-Thiadiazole Derivatives as Inhibitors of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2)

Angiogenesis plays a pivotal role in the growth, survival, and metastasis of solid tumors, with Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) being overexpressed in many human solid tumors, making it an appealing target for anti-cancer therapies. This study aimed to identify potential lead compounds with azole moiety exhibiting VEGFR-2 inhibitory effects. A ligand-based pharmacophore model was constructed using the X-ray crystallographic structure of VEGFR-2 complexed with tivozanib (PDB ID: 4ASE) to screen the ZINC15 database. Following virtual screening, six compounds demonstrated promising docking scores and drug-likeness comparable to tivozanib. These hits underwent detailed pharmacokinetic analysis to assess their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Furthermore, Density Functional Theory (DFT) analysis was employed to investigate the molecular orbital properties of the top hits from molecular docking. Molecular dynamics (MD) simulations were conducted to evaluate the conformational stability of the complexes over a 100 ns run. Results indicated that the compounds (ZINC8914312, ZINC8739578, ZINC8927502, and ZINC17138581) exhibited the most promising lead requirements for inhibiting VEGFR-2 and suppressing angiogenesis in cancer therapy. This integrated approach, combining pharmacophore modeling, molecular docking, ADMET studies, DFT analysis, and MD simulations, provides valuable insights into the identification of potential anti-cancer agents targeting VEGFR-2.

Discovery of a novel Xanthone derivative P24 for anti-AD via targeting sTGFBR3

Soluble transforming growth factor beta receptor 3 (sTGFBR3) antagonist is a new focus in the research and development of Alzheimer's disease (AD) drugs. Our previous studies have identified sTGFBR3 as a promising new target for AD, with few targeted antagonists identified. In this study, we performed structural modeling of sTGFBR3 using AlphaFold2, followed by high-throughput virtual screening and surface plasmon resonance assays. which collectively identified Xanthone as potential compounds for targeting sTGFBR3. After optimizing the sTGFBR3-Xanthone complex using molecular dynamics (MD) simulations, we prepared a series of novel Xanthone derivatives and evaluated their anti-inflammatory activity, toxicity, and structure-activity relationship in BV2 cell model induced by lipopolysaccharides (LPS) or APP/PS1/tau mouse brain extract (BE). Several derivatives with the most potent anti-inflammatory activity were tested for blood-brain barrier permeability and sTGFBR3 affinity. Derivative P24, selected for its superior properties, was further evaluated in vitro. The results indicated that P24 increased the activation of TGF-β signaling and decreased the activation of IκBα/NF-κB signaling by targeting sTGFBR3, thereby regulating the inflammation-phagocytosis balance in microglia. Moreover, the low acute toxicity, long half-life, and low plasma clearance of P24 suggest that it can be sustained in vivo. This property may render P24 a more effective treatment modality for chronic diseases, particularly AD. The study demonstrates P24 serve as potential novel candidates for the treatment of AD via antagonizing sTGFBR3.

Computational targeting of iron uptake proteins in Covid-19 induced mucormycosis to identify inhibitors via molecular dynamics, molecular mechanics and density function theory studies

Mucormycosis is a concerning invasive fungal infection with difficult diagnosis, high mortality rates, and limited treatment options. Iron availability is crucial for fungal growth that causes this disease. This study aimed to computationally target iron uptake proteins in Rhizopus arrhizus, Lichtheimia corymbifera, and Mucor circinelloides to identify inhibitors, thereby halting fungal growth and intervening in mucormycosis pathogenesis. Seven important iron uptake proteins were identified, modeled, and validated using Ramachandran plots. An in-house antifungal library of ~ 15,401 compounds was screened in molecular docking studies with these proteins. The best small molecule-protein complexes were simulated at 100 ns using Maestro, Schrodinger. Toxicity predictions suggested all six molecules, identified as the best binding compounds to seven proteins, belonged to lower toxicity levels per GHS classification. A molecular mechanics GBSA study for all seven complexes indicated low standard deviations after calculating free binding energies every 10 ns of the 100 ns trajectory. Density functional theory via quantum mechanics approaches highlighted the HOMO, LUMO, and other properties of the six best-bound molecules, revealing their binding capabilities and behaviour. This study sheds light on the molecular mechanisms and protein-ligand interactions, providing a multi-dimensional view towards the use of FDBD01920, FDBD01923, and FDBD01848 as stable antifungal ligands.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00264-7.

Discovery of a potent CDKs/FLT3 PROTAC with enhanced differentiation and proliferation inhibition for AML

AML is an aggressive malignancy of immature myeloid progenitor cells. Discovering effective treatments for AML through cell differentiation and anti-proliferation remains a significant challenge. Building on previous studies on CDK2 PROTACs with differentiation-inducing properties, this research aims to enhance CDKs degradation through structural optimization to facilitate the differentiation and inhibit the proliferation of AML cells. Compound C3, featuring a 4-methylpiperidine ring linker, effectively degraded CDK2 with a DC50 value of 18.73 ± 10.78 nM, and stimulated 72.77 ± 3.51 % cell differentiation at 6.25 nM in HL-60 cells. Moreover, C3 exhibited potent anti-proliferative activity against various AML cell types. Degradation selectivity analysis indicated that C3 could be endowed with efficient degradation of CDK2/4/6/9 and FLT3, especially FLT3-ITD in MV4-11 cells. These findings propose that C3 combined targeting CDK2/4/6/9 and FLT3 with enhanced differentiation and proliferation inhibition, which holds promise as a potential treatment for AML.

An artificial intelligence accelerated virtual screening platform for drug discovery

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Caught between a ROCK and a hard place: current challenges in structure-based drug design

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

Discovery of an Exceptionally Orally Bioavailable and Potent HPK1 PROTAC with Enhancement of Antitumor Efficacy of Anti-PD-L1 Therapy

HPK1, a well-known negative regulator of T cell receptors, can cause T cell dysfunction when abnormally activated. In this study, a PROTAC C3 was designed and synthesized by optimizing the physicochemical properties of the warhead, linker, and CRBN ligand. C3 demonstrated significant HPK1 degradation with a DC50 of 21.26 nM, excellent oral absorption with a Cmax of 10,899.92 ng/mL, and a bioavailability (F %) of 81.7%. C3 also showed degradation selectivity and potent immune activation effects. Proteomic and WB analyses revealed that immune-activating effect of C3 is attributed to the inhibition of SLP76 and NF-κB signaling pathways, as well as the enhancement of MAPK signaling pathway transduction. In vivo efficacy study demonstrated that oral administration of C3 in combination with anti-PDL1 antibody significantly inhibited tumor growth (tumor growth inhibition = 65.58%). These findings suggest that C3, a novel HPK1 PROTAC, holds promise as a therapeutic agent for tumor immunotherapy.

Discovery of Novel Acethydrazide-Containing Flavonol Derivatives as Potential Antifungal Agents

In this study, a series of novel hydrazide-containing flavonol derivatives was designed, synthesized, and evaluated for antifungal activity. In the in vitro antifungal assay, most of the target compounds exhibited potent antifungal activity against seven tested phytopathogenic fungi. In particular, compound C32 showed the best antifungal activity against Rhizoctonia solani (EC50 = 0.170 μg/mL), outperforming carbendazim (EC50 = 0.360 μg/mL) and boscalid (EC50 = 1.36 μg/mL). Compound C24 exhibited excellent antifungal activity against Valsa mali, Botrytis cinerea, and Alternaria alternata with EC50 values of 0.590, 0.870, and 1.71 μg/mL, respectively. The in vivo experiments revealed that compounds C32 and C24 were potential novel agricultural antifungals. 3D quantitative structure-activity relationship (3D-QSAR) models were used to analyze the structure-activity relationships of these compounds. The analysis results indicated that introducing appropriate electronegative groups at position 4 of a benzene ring could effectively improve the anti-R. solani activity. In the antifungal mechanism study, scanning electron microscopy and transmission electron microscopy analyses revealed that C32 disrupted the normal growth of hyphae by affecting the structural integrity of the cell membrane and cellular respiration. Furthermore, compound C32 exhibited potent succinate dehydrogenase (SDH) inhibitory activity (IC50 = 8.42 μM), surpassing that of the SDH fungicide boscalid (IC50 = 15.6 μM). The molecular dynamics simulations and docking experiments suggested that compound C32 can occupy the active site and form strong interactions with the key residues of SDH. Our findings have great potential for aiding future research on plant disease control in agriculture.

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.

Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active-while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks

Message passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art performance on protein-ligand complex scoring tasks. Here, we describe the proximity graph network (PGN) package, an open-source toolkit that constructs ligand-receptor graphs based on atom proximity and allows users to rapidly apply and evaluate MPNN architectures for a broad range of tasks. We demonstrate the utility of PGN by introducing benchmarks for affinity and docking score prediction tasks. Graph networks generalize better than fingerprint-based models and perform strongly for the docking score prediction task. Overall, MPNNs with proximity graph data structures augment the prediction of ligand-receptor complex properties when ligand-receptor data are available.

Essential Oils of Laurus nobilis L.: From Chemical Analysis to In Silico Investigation of Anti-Inflammatory Activity by Soluble Epoxide Hydrolase (sEH) Inhibition

Laurus nobilis L. is commonly used in folk medicine in the form of infusion or decoction to treat gastrointestinal diseases and flatulence as a carminative, antiseptic, and anti-inflammatory agent. In this study, the essential oil (EO) composition of wild-grown L. nobilis L. leaves collected from seven different altitudinal locations in the Molise region and adjacent regions (Abruzzo and Campania) was investigated. EOs from the leaves were obtained by hydrodistillation and analyzed by GC-FID and GC/MS, and 78 compounds were identified. The major oil components were 1,8-cineol (43.52-31.31%), methyl-eugenol (14.96-4.07%), α-terpinyl acetate (13.00-8.51%), linalool (11.72-1.08%), sabinene (10.57-4.85%), α-pinene (7.41-3.61%), eugenol (4.12-1.97%), and terpinen-4-ol (2.33-1.25%). Chemometric techniques have been applied to compare the chemical composition. To shed light on the nutraceutical properties of the main hydrophobic secondary metabolites (≥1.0%) of laurel EOs, we assessed the in vitro antioxidant activities based on 2,2-diphenyl-1-picrylhydrazyl (DPPH•) radical scavenging activity and the reducing antioxidant power by using a ferric reducing power (FRAP) assay. Furthermore, we highlighted the anti-inflammatory effects of seven EOs able to interfere with the enzyme soluble epoxide hydrolase (sEH), a key enzyme in the arachidonic acid cascade, in concentrations ranging from 16.5 ± 4.3 to 8062.3 ± 580.9 mg/mL. Thanks to in silico studies, we investigated and rationalized the observed anti-inflammatory properties, ascribing the inhibitory activity toward the disclosed target to the most abundant volatile phytochemicals (≥1.0%) of seven EOs.

Quinoline-based Schiff bases as possible antidiabetic agents: ligand-based pharmacophore modeling, 3D QSAR, docking, and molecular dynamics simulations study

α-Glucosidase enzyme inhibition is a legitimate approach to combat type 2 diabetes mellitus as it manages to control postprandial hyperglycemia. In this pursuit, a literature search identified quinoline-based molecules as potential α-glucosidase inhibitors. Thus our intended approach is to identify pharmacophoric features responsible for the α-glucosidase inhibition. This was achieved by performing, ligand-based pharmacophore modeling, 3D QSAR model development, pharmacophore-based screening of a rationally designed quinoline-based benzohydrazide Schiff base library, identifying, synthesizing and characterizing molecules (6a-6j) by IR, (1H and 13C) NMR, and mass studies. Further, these molecules were evaluated for α-glucosidase and α-amylase inhibitory potential. Compound 6c was found to inhibit α-glucosidase enzyme with an IC50 value of 12.95 ± 2.35 μM. Similarly, compound 6b was found to have an IC50 value of 19.37 ± 0.96 μM as compared to acarbose (IC50: 32.63 ± 1.07 μM); the inhibitory kinetics of compounds 6b and 6c revealed a competitive type of inhibition; the inhibitory effect can be attributed to its mapped pharmacophoric feature and model validation with a survival score of 5.0697 and vector score of 0.9552. The QSAR model showed a strong correlation with an R 2 value of 0.96. All the compounds (6a-6j) showed no toxicity in L929 cell lines by the MTT assay method. Further, the binding orientation and stability of the molecules were assessed using molecular docking studies and MD trajectory analysis. The energy profile of the molecules with protein as a complex and molecules alone was evaluated using MM/GBSA and DFT calculations, respectively; finally, the pharmacokinetic profile was computed using ADMET analysis.

The impact of Library Size and Scale of Testing on Virtual Screening

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the σ2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

Dual-target drug design has gained significant attention in the treatment of complex diseases, such as cancers and autoimmune disorders. A widely employed design strategy is combining pharmacophores to leverage the knowledge of structure-activity relationships of both targets. Unfortunately, pharmacophore combination often struggles with long and expensive trial and error, because the protein pockets of the two targets impose complex structural constraints. In this study, we propose AIxFuse, a structure-aware dual-target drug design method that learns pharmacophore fusion patterns to satisfy the dual-target structural constraints simulated by molecular docking. AIxFuse employs two self-play reinforcement learning (RL) agents to learn pharmacophore selection and fusion by comprehensive feedback including dual-target molecular docking scores. Collaboratively, the molecular docking scores are learned by active learning (AL). Through collaborative RL and AL, AIxFuse learns to generate molecules with multiple desired properties. AIxFuse is shown to outperform state-of-the-art methods in generating dual-target drugs against glycogen synthase kinase-3 beta (GSK3β) and c-Jun N-terminal kinase 3 (JNK3). When applied to another task against retinoic acid receptor-related orphan receptor γ-t (RORγt) and dihydroorotate dehydrogenase (DHODH), AIxFuse exhibits consistent performance while compared methods suffer from performance drops, leading to a 5 times higher performance in success rate. Docking studies demonstrate that AIxFuse can generate molecules concurrently satisfying the binding mode required by both targets. Further free energy perturbation calculation indicates that the generated candidates have promising binding free energies against both targets.

Application of Funnel Metadynamics to the Platelet Integrin αIIbβ3 in Complex with an RGD Peptide

Integrin αIIbβ3 mediates platelet aggregation by binding the Arginyl-Glycyl-Aspartic acid (RGD) sequence of fibrinogen. RGD binding occurs at a site topographically proximal to the αIIb and β3 subunits, promoting the conformational activation of the receptor from bent to extended states. While several experimental approaches have characterized RGD binding to αIIbβ3 integrin, applying computational methods has been significantly more challenging due to limited sampling and the need for a priori information regarding the interactions between the RGD peptide and integrin. In this study, we employed all-atom simulations using funnel metadynamics (FM) to evaluate the interactions of an RGD peptide with the αIIb and β3 subunits of integrin. FM incorporates an external history-dependent potential on selected degrees of freedom while applying a funnel-shaped restraint potential to limit RGD exploration of the unbound state. Furthermore, it does not require a priori information about the interactions, enhancing the sampling at a low computational cost. Our FM simulations reveal significant molecular changes in the β3 subunit of integrin upon RGD binding and provide a free-energy landscape with a low-energy binding mode surrounded by higher-energy prebinding states. The strong agreement between previous experimental and computational data and our results highlights the reliability of FM as a method for studying dynamic interactions of complex systems such as integrin.

Emerging structure-based computational methods to screen the exploding accessible chemical space

Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.

The present state and challenges of active learning in drug discovery

Active learning (AL) is an iterative feedback process that efficiently identifies valuable data within vast chemical space, even with limited labeled data. This characteristic renders it a valuable approach to tackle the ongoing challenges faced in drug discovery, such as the ever-expanding explore space and the limitations of labeled data. Consequently, AL is increasingly gaining prominence in the field of drug development. In this paper, we comprehensively review the application of AL at all stages of drug discovery, including compounds-target interaction prediction, virtual screening, molecular generation and optimization, as well as molecular properties prediction. Additionally, we discuss the challenges and prospects associated with the current applications of AL in drug discovery.

Live cell screening to identify RNA-binding small molecule inhibitors of the pre-let-7-Lin28 RNA-protein interaction

Dysregulation of the networking of RNA-binding proteins (RBPs) and RNAs drives many human diseases, including cancers, and the targeting of RNA-protein interactions (RPIs) has emerged as an exciting area of RNA-targeted drug discovery. Accordingly, methods that enable the discovery of cell-active small molecule modulators of RPIs are needed to propel this emerging field forward. Herein, we describe the application of live-cell assay technology, RNA interaction with protein-mediated complementation assay (RiPCA), for high-throughput screening to identify small molecule inhibitors of the pre-let-7d-Lin28A RPI. Utilizing a combination of RNA-biased small molecules and virtual screening hits, we discovered an RNA-binding small molecule that can disrupt the pre-let-7-Lin28 interaction demonstrating the potential of RiPCA for advancing RPI-targeted drug discovery.

Targeting ATP-binding site of WRN Helicase: Identification of novel inhibitors through pocket analysis and Molecular Dynamics-Enhanced virtual screening

WRN helicase is a critical protein involved in maintaining genomic stability, utilizing ATP hydrolysis to dissolve DNA secondary structures. It has been identified as a promising synthetic lethal target for microsatellite instable (MSI) cancers. However, few WRN helicase inhibitors have been discovered, and their potential binding sites remain unexplored. In this study, we analyzed potential binding sites for WRN inhibitors and focused on the ATP-binding site for screening new inhibitors. Through molecular dynamics-enhanced virtual screening, we identified two compounds, h6 and h15, which effectively inhibited WRN's helicase and ATPase activity in vitro. Importantly, these compounds selectively targeted WRN's ATPase activity, setting them apart from other non-homologous proteins with ATPase activity. In comparison to the homologous protein BLM, h6 exhibits some degree of selectivity towards WRN. We also investigated the binding mode of these compounds to WRN's ATP-binding sites. These findings offer a promising strategy for discovering new WRN inhibitors and present two novel scaffolds, which might be potential for the development of MSI cancer treatment.

Blockade of pan-viral propagation by inhibition of host cell PNPT1

For successful viral propagation within infected cells, the virus needs to overcome the cellular integrated stress response (ISR), triggered during viral infection, which, in turn, inhibits general protein translation. This paper reports a tactic employed by viruses to suppress the ISR by upregulating host cell polyribonucleotide nucleotidyltransferase 1 (PNPT1). The propagation of adenovirus, murine cytomegalovirus and hepatovirus within their respective host cells induces PNPT1 expression. Notably, when PNPT1 is knocked down, the propagation of all three viruses is prevented. Mechanistically, the inhibition of PNPT1 facilitates the relocation of mitochondrial double-stranded RNAs (mt-dsRNAs) to the cytoplasm, where they activate RNA-activated protein kinase (PKR). This activation leads to eukaryotic initiation factor 2α (eIF2α) phosphorylation, resulting in the suppression of translation. Furthermore, by scrutinizing the PNPT1 recognition element and screening 17,728 drugs and bioactive compounds approved by the US Food and Drug Administration, lanatoside C was identified as a potent PNPT1 inhibitor. This compound impedes the propagation of adenovirus, murine cytomegalovirus and hepatovirus, and suppresses production of the severe acute respiratory syndrome coronavirus-2 spike protein. These discoveries shed light on a novel strategy to impede pan-viral propagation by activating the host cell mt-dsRNA-PKR-eIF2α signalling axis.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking

Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.

Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach

The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

Artificial intelligence for drug discovery and development in Alzheimer's disease

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.

Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction

Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular libraries. To design a robust AL protocol, it is important to understand the influence of AL parameters, as well as the features of the data sets on the outcomes. We use four affinity data sets for different targets (TYK2, USP7, D2R, Mpro) to systematically evaluate the performance of machine learning models [Gaussian process (GP) model and Chemprop model], sample selection protocols, and the batch size based on metrics describing the overall predictive power of the model (R2, Spearman rank, root-mean-square error) as well as the accurate identification of top 2%/5% binders (Recall, F1 score). Both models have a comparable Recall of top binders on large data sets, but the GP model surpasses the Chemprop model when training data are sparse. A larger initial batch size, especially on diverse data sets, increased the Recall of both models as well as overall correlation metrics. However, for subsequent cycles, smaller batch sizes of 20 or 30 compounds proved to be desirable. Furthermore, adding artificial Gaussian noise to the data up to a certain threshold still allowed the model to identify clusters with top-scoring compounds. However, excessive noise (<1σ) did impact the model's predictive and exploitative capabilities.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

Advancing material property prediction: using physics-informed machine learning models for viscosity

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.