Exploring the activation mechanism of metabotropic glutamate receptor 2.

Homomeric dimerization of metabotropic glutamate receptors (mGlus) is essential for the modulation of their functions and represents a promising avenue for the development of novel therapeutic approaches to address central nervous system diseases. Yet, the scarcity of detailed molecular and energetic data on mGlu2 impedes our in-depth comprehension of their activation process. Here, we employ computational simulation methods to elucidate the activation process and key events associated with the mGlu2, including a detailed analysis of its conformational transitions, the binding of agonists, Gi protein coupling, and the guanosine diphosphate (GDP) release. Our results demonstrate that the activation of mGlu2 is a stepwise process and several energy barriers need to be overcome. Moreover, we also identify the rate-determining step of the mGlu2's transition from the agonist-bound state to its active state. From the perspective of free-energy analysis, we find that the conformational dynamics of mGlu2's subunit follow coupled rather than discrete, independent actions. Asymmetric dimerization is critical for receptor activation. Our calculation results are consistent with the observation of cross-linking and fluorescent-labeled blot experiments, thus illustrating the reliability of our calculations. Besides, we also identify potential key residues in the Gi protein binding position on mGlu2, mGlu2 dimer's TM6-TM6 interface, and Gi α5 helix by the change of energy barriers after mutation. The implications of our findings could lead to a more comprehensive grasp of class C G protein-coupled receptor activation.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

Structure-based virtual screening of novel USP5 inhibitors targeting the zinc finger ubiquitin-binding domain.

The equilibrium of cellular protein levels is pivotal for maintaining normal physiological functions. USP5 belongs to the deubiquitination enzyme (DUBs) family, controlling protein degradation and preserving cellular protein homeostasis. Aberrant expression of USP5 is implicated in a variety of diseases, including cancer, neurodegenerative diseases, and inflammatory diseases. In this paper, a multi-level virtual screening (VS) approach was employed to target the zinc finger ubiquitin-binding domain (ZnF-UBD) of USP5, leading to the identification of a highly promising candidate compound 0456-0049. Molecular dynamics (MD) simulations were then employed to assess the stability of complex binding and predict hotspot residues in interactions. The results indicated that the candidate stably binds to the ZnF-UBD of USP5 through crucial interactions with residues ARG221, TRP209, GLY220, ASN207, TYR261, TYR259, and MET266. Binding free energy calculations, along with umbrella sampling (US) simulations, underscored a superior binding affinity of the candidate relative to known inhibitors. Moreover, US simulations revealed conformational changes of USP5 during ligand dissociation. These insights provide a valuable foundation for the development of novel inhibitors targeting USP5.

A general model for predicting enzyme functions based on enzymatic reactions.

Accurate prediction of the enzyme comission (EC) numbers for chemical reactions is essential for the understanding and manipulation of enzyme functions, biocatalytic processes and biosynthetic planning. A number of machine leanring (ML)-based models have been developed to classify enzymatic reactions, showing great advantages over costly and long-winded experimental verifications. However, the prediction accuracy for most available models trained on the records of chemical reactions without specifying the enzymatic catalysts is rather limited. In this study, we introduced BEC-Pred, a BERT-based multiclassification model, for predicting EC numbers associated with reactions. Leveraging transfer learning, our approach achieves precise forecasting across a wide variety of Enzyme Commission (EC) numbers solely through analysis of the SMILES sequences of substrates and products. BEC-Pred model outperformed other sequence and graph-based ML methods, attaining a higher accuracy of 91.6%, surpassing them by 5.5%, and exhibiting superior F1 scores with improvements of 6.6% and 6.0%, respectively. The enhanced performance highlights the potential of BEC-Pred to serve as a reliable foundational tool to accelerate the cutting-edge research in synthetic biology and drug metabolism. Moreover, we discussed a few examples on how BEC-Pred could accurately predict the enzymatic classification for the Novozym 435-induced hydrolysis and lipase efficient catalytic synthesis. We anticipate that BEC-Pred will have a positive impact on the progression of enzymatic research.

ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches.

Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-ß-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell wall of Mtb and represents a promising target for anti-TB drug development. Therefore, there is an urgent need to discover DprE1 inhibitors with novel scaffolds, improved bioactivity and high drug-likeness. Recent studies have shown that artificial intelligence/computer-aided drug design (AI/CADD) techniques are powerful tools in the discovery of novel DprE1 inhibitors. This review provides an overview of the discovery of DprE1 inhibitors and their underlying mechanism of action and highlights recent advances in the discovery and optimization of DprE1 inhibitors using AI/CADD approaches.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing.

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.

Discovery of Novel Anti-Resistance AR Antagonists Guided by Funnel Metadynamics Simulation.

Androgen receptor (AR) antagonists are widely used for the treatment of prostate cancer (PCa), but their therapeutic efficacy is usually compromised by the rapid emergence of drug resistance. However, the lack of the detailed interaction between AR and its antagonists poses a major obstacle to the design of novel AR antagonists. Here, funnel metadynamics is employed to elucidate the inherent regulation mechanisms of three AR antagonists (hydroxyflutamide, enzalutamide, and darolutamide) on AR. For the first time it is observed that the binding of antagonists significantly disturbed the C-terminus of AR helix-11, thereby disrupting the specific internal hydrophobic contacts of AR-LBD and correspondingly the communication between AR ligand binding pocket (AR-LBP), activation function 2 (AF2), and binding function 3 (BF3). The subsequent bioassays verified the necessity of the hydrophobic contacts for AR function. Furthermore, it is found that darolutamide, a newly approved AR antagonist capable of fighting almost all reported drug resistant AR mutants, can induce antagonistic binding structure. Subsequently, docking-based virtual screening toward the dominant binding conformation of AR for darolutamide is conducted, and three novel AR antagonists with favorable binding affinity and strong capability to combat drug resistance are identified by in vitro bioassays. This work provides a novel rational strategy for the development of anti-resistant AR antagonists.

Identification of 3-aryl-5-methyl-isoxazole-4-carboxamide derivatives and analogs as novel HIF-2α agonists through docking-based virtual screening and structural modification.

Hypoxia-inducible factor-2 (HIF-2) serves as the pivotal transcription factor in cellular responses to low oxygen levels, particularly concerning the regulation of erythropoietin (EPO) production. A docking-based virtual screening on crystal structures of HIF-2α inhibitors unexpectedly identified 3-phenyl-5-methyl-isoxazole-4-carboxamide derivative v19 as a hit of HIF-2α agonist. Further structural optimizations of compound v19 led to the discovery of a series of HIF-2α agonists with novel scaffolds. The most promising compounds 12g and 14d exhibited potent HIF-2α agonistic activities in vitro with EC50 values of 2.29 µM and 1.78 µM, respectively. Molecular dynamics simulations have revealed their capacity to allosterically enhance HIF-2 dimerization, which shed light on their mechanism of action. Moreover, compound 14d demonstrated a favorable pharmacokinetic (PK) profile, boasting an impressive oral bioavailability value of 68.71 %. These findings strongly suggest that compound 14d is an auspicious lead compound for the treatment of renal anemia.

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space.

Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.

Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization.

Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.

OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds.

Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.

Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning.

Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information. Subsequently, by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning, we proposed a powerful bidirectional encoder representations from transformers (BERT)-based reaction yield predictor named Egret. It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset. We found that reaction-condition-based contrastive learning enhances the model's sensitivity to reaction conditions, and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions. Furthermore, we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes. Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules. In addition, through meta-learning strategy, we further improved the reliability of the model's prediction for reaction types with limited data and lower data quality. Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.

CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training.

The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.

RediscMol: Benchmarking Molecular Generation Models in Biological Properties.

Deep learning-based molecular generative models have garnered emerging attention for their capability to generate molecules with novel structures and desired physicochemical properties. However, the evaluation of these models, particularly in a biological context, remains insufficient. To address the limitations of existing metrics and emulate practical application scenarios, we construct the RediscMol benchmark that comprises active molecules extracted from 5 kinase and 3 GPCR data sets. A set of rediscovery- and similarity-related metrics are introduced to assess the performance of 8 representative generative models (CharRNN, VAE, Reinvent, AAE, ORGAN, RNNAttn, TransVAE, and GraphAF). Our findings based on the RediscMol benchmark differ from those of previous evaluations. CharRNN, VAE, and Reinvent exhibit a greater ability to reproduce known active molecules, while RNNAttn, TransVAE, and GraphAF struggle in this aspect despite their notable performance on commonly used distribution-learning metrics. Our evaluation framework may provide valuable guidance for advancing generative models in real-world drug design scenarios.

Discovery of N-(1-(6-Oxo-1,6-dihydropyrimidine)-pyrazole) Acetamide Derivatives as Novel Noncovalent DprE1 Inhibitors against Mycobacterium tuberculosis.

Decaprenylphosphoryl-ß-d-ribose oxidase (DprE1) is a promising target for treating tuberculosis (TB). Currently, most novel DprE1 inhibitors are discovered through high-throughput screening, while computer-aided drug design (CADD) strategies are expected to promote the discovery process. In this study, with the aid of structure-based virtual screening and computationally guided design, a series of novel scaffold N-(1-(6-oxo-1,6-dihydropyrimidine)-pyrazole) acetamide derivatives with significant antimycobacterial activities were identified. Among them, compounds LK-60 and LK-75 are capable of effectively suppressing the proliferation of Mtb with MICMtb values of 0.78-1.56 µM, comparable with isoniazid and much superior to the phase II candidate TBA-7371 (MICMtb = 12.5 µM). LK-60 is also the most active DprE1 inhibitor derived from CADD so far. Further studies confirmed their high affinity to DprE1, good safety profiles to gut microbiota and human cells, and synergy effects with either rifampicin or ethambutol, indicating their broad potential for clinical applications.