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The free energy perturbation (FEP) method is a powerful technique for accurate binding free energy calculations, which is crucial for identifying potent ligands with a high affinity in drug discovery. However, the widespread application of FEP is limited by the high computational cost required to achieve equilibrium sampling and the challenges in obtaining converged predictions. In this study, we present the convergence-adaptive roundtrip (CAR) method, which is an enhanced adaptive sampling approach, to address the key challenges in FEP calculations, including the precision-efficiency tradeoff, sampling efficiency, and convergence assessment. By employing on-the-fly convergence analysis to automatically adjust simulation times, enabling efficient traversal of the important phase space through rapid propagation of conformations between different states and eliminating the need for multiple parallel simulations, the CAR method increases convergence and minimizes computational overhead while maintaining calculation accuracy. The performance of the CAR method was evaluated through relative binding free energy (RBFE) calculations on benchmarks comprising four diverse protein-ligand systems. The results demonstrated a significant speedup of over 8-fold compared to conventional FEP methods while maintaining high accuracy. The overall R2 values of 0.65 and 0.56 were obtained using the combined-structure FEP approach and the single-step FEP approach, respectively, in conjunction with the CAR method. In-depth case studies further highlighted the superior performance of the CAR method in terms of convergence acceleration, improved predicted correlations, and reduced computational costs. The advancement of the CAR method makes it a highly effective approach, enhancing the applicability of FEP in drug discovery.

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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.

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Acute myelogenous leukemia (AML) is the most common form of acute leukemia in adults. PDE1 (Phosphodiesterase 1) is a subfamily of the PDE super-enzyme families that can hydrolyze the second messengers cAMP and cGMP simultaneously. Previous research has shown that suppressing the gene expression of PDE1 can trigger apoptosis of human leukemia cells. However, no selective PDE1 inhibitors have been used to explore whether PDE1 is a potential target for treating AML. Based on our previously reported PDE9/PDE1 dual inhibitor 11a, a series of novel pyrazolopyrimidinone derivatives were designed in this study. The lead compound 6c showed an IC50 of 7.5 nM against PDE1, excellent selectivity over other PDEs and good metabolic stability. In AML cells, compound 6c significantly inhibited the proliferation and induced apoptosis. Further experiments indicated that the apoptosis induced by 6c was through a mitochondria-dependent pathway by decreasing the ratio of Bcl-2/Bax and increasing the cleavage of caspase-3, 7, 9, and PARP. All these results suggested that PDE1 might be a novel target for AML.

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The accurate prediction of the binding affinities between small molecules and biological macromolecules plays a fundamental role in structure-based drug design, which is still challenging. The free energy perturbation-based absolute binding free energy (FEP-ABFE) approach has shown potential in its reliability. To correctly calculate the energy related to the ligand being restrained by the receptor, additional restraints between the ligand and the receptor are needed. However, determining the restraint parameters for individual ligands empirically is too trivial to be automated, and usually gives rise to numerical instabilities, which set back the applications of FEP-ABFE. To address these issues, we derived the analytical expression for the probability distribution of energy differences, P(ΔU), during the process of restraint addition, which is called the RED-E (restraint energy distribution at equilibrium position) function. Simulations indicated that the RED-E function can accurately describe P(ΔU) when restraints are added at the equilibrium position. Based on the RED-E function, an automatic restraint selection method was proposed to select the best restraint. With this method, there is a high phase-space overlap between the free and restrained states, such that using a 2-λ perturbation can accurately calculate the free energy of the restraint addition, which is a nearly 6 times acceleration compared with current widely used 12-λ perturbation method. The RED-E function gives insight into the non-Gaussian behavior of the sampled P(ΔU) in certain FEP processes in an analytical way. The highly automated and accelerated restraint selection also makes it possible for the large-scale application of FEP-ABFE in real drug discovery practices.

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In the process of drug discovery, one of the key problems is how to improve the biological activity and ADMET properties starting from a specific structure, which is also called structural optimization. Based on a starting scaffold, the use of deep generative model to generate molecules with desired drug-like properties will provide a powerful tool to accelerate the structural optimization process. However, the existing generative models remain challenging in extracting molecular features efficiently in 3D space to generate drug-like 3D molecules. Moreover, most of the existing ADMET prediction models made predictions of different properties through a single model, which can result in reduced prediction accuracy on some datasets. To effectively generate molecules from a specific scaffold and provide basis for the structural optimization, the 3D-SMGE (3-Dimensional Scaffold-based Molecular Generation and Evaluation) work consisting of molecular generation and prediction of ADMET properties is presented. For the molecular generation, we proposed 3D-SMG, a novel deep generative model for the end-to-end design of 3D molecules. In the 3D-SMG model, we designed the cross-aggregated continuous-filter convolution (ca-cfconv), which is used to achieve efficient and low-cost 3D spatial feature extraction while ensuring the invariance of atomic space rotation. 3D-SMG was proved to generate valid, unique and novel molecules with high drug-likeness. Besides, the proposed data-adaptive multi-model ADMET prediction method outperformed or maintained the best evaluation metrics on 24 out of 27 ADMET benchmark datasets. 3D-SMGE is anticipated to emerge as a powerful tool for hit-to-lead structural optimizations and accelerate the drug discovery process.

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Phosphodiesterase 1 (PDE1) is a subfamily of PDE super enzyme families that can hydrolyze cyclic adenosine monophosphate and cyclic guanosine monophosphate simultaneously. Currently, the number of PDE1 inhibitors is relatively few, significantly limiting their application. Herein, a novel series of quinolin-2(1H)-ones were designed rationally, leading to compound 10c with an IC50 of 15 nM against PDE1C, high selectivity across other PDEs, and remarkable safety properties. Furthermore, we used the lead compound 10c as a chemical tool to explore whether PDE1 could work as a novel potential target for the treatment of inflammatory bowel disease (IBD), a disease which is a chronic, relapsing disorder of the gastrointestinal tract inflammation lacking effective treatment. Our results showed that administration of 10c exerted significant anti-IBD effects in the dextran sodium sulfate-induced mice model and alleviated the inflammatory response, indicating that PDE1 could work as a potent target for IBD.

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Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle closure (cc) has proven to be an effective method in improving the calculation accuracy by correcting the hysteresis (summation of errors) of each closed cycle to the theoretical value 0. However, a primary hypothesis was made in the current cycle closure method that the hysteresis is evenly distributed to all paths, which is unlikely to be true in practice and may limit the further improvement of the calculation accuracy when better error estimation methods are available. Moreover, being a closed source software makes the current cycle closure method unachievable in many studies. In this paper, a newly implemented open source graph-based weighted cycle closure (wcc) algorithm was developed and introduced, not only including functions from the original cc method but also containing a new wcc method which can consider different error contributions from different paths and further improve the calculation accuracy. The wcc program also provides a new path-independent molecular error calculation method, which can be quite useful in many studies (like structure-activity relationship (SAR)) compared with the path-dependent method of the original cc program.

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As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE-based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

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Our previous research demonstrated that phosphodiesterase-1 (PDE1) could work as a potential target against idiopathic pulmonary fibrosis. Nimodipine, a calcium antagonist commonly used to improve hypertension, was reported to have inhibition against PDE1. Herein, a series of nimodipine analogues were discovered as novel selective and potent PDE1 inhibitors after structural modifications. Compound 2g exhibited excellent inhibitory activity against PDE1C (IC50 = 10 nM), high selectivity over other PDEs except for PDE4, and weak calcium channel antagonistic activity. Administration of compound 2g exhibited remarkable therapeutic effects in a rat model of pulmonary fibrosis induced by bleomycin and prevented myofibroblast differentiation induced by TGF-ß1. The expressions of PDE1B and PDE1C were found to be increased and concentrated in the focus of fibrosis. Compound 2g increased the levels of 3',5'-cyclic adenosine monophosphate (cAMP) and 3',5'-cyclic guanosine monophosphate (cGMP) in the lungs of rats with pulmonary fibrosis, supporting the fact that the anti-fibrosis effects of 2g were through the regulation of cAMP and cGMP.

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Dipyridamole, apart from its well-known antiplatelet and phosphodiesterase inhibitory activities, is a promising old drug for the treatment of pulmonary fibrosis. However, dipyridamole shows poor pharmacokinetic properties with a half-life (T1/2) of 7 min in rat liver microsomes (RLM). To improve the metabolic stability of dipyridamole, a series of pyrimidopyrimidine derivatives have been designed with the assistance of molecular docking. Among all the twenty-four synthesized compounds, compound (S)-4h showed outstanding metabolic stability (T1/2 = 67 min) in RLM, with an IC50 of 332 nM against PDE5. Furthermore, some interesting structure-activity relationships (SAR) were explained with the assistance of molecular docking.

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Scaffold hopping refers to computer-aided screening for active compounds with different structures against the same receptor to enrich privileged scaffolds, which is a topic of high interest in organic and medicinal chemistry. However, most approaches cannot efficiently predict the potency level of candidates after scaffold hopping. Herein, we identified potent PDE5 inhibitors with a novel scaffold via a free energy perturbation (FEP)-guided scaffold-hopping strategy, and FEP shows great advantages to precisely predict the theoretical binding potencies ΔG FEP between ligands and their target, which were more consistent with the experimental binding potencies ΔG EXP (the mean absolute deviations | Δ G FEP - Δ G EXP |  < 2 kcal/mol) than those ΔG MM-PBSA or ΔG MM-GBSA predicted by the MM-PBSA or MM-GBSA method. Lead L12 had an IC50 of 8.7 nmol/L and exhibited a different binding pattern in its crystal structure with PDE5 from the famous starting drug tadalafil. Our work provides the first report via the FEP-guided scaffold hopping strategy for potent inhibitor discovery with a novel scaffold, implying that it will have a variety of future applications in rational molecular design and drug discovery.

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In situ imaging of DNA repair enzymes in living cells gives important insights to diagnosis and explore the formation of various diseases. Fluorescent probes have become a powerful and widely used technique for their high sensitivity and real-time capabilities, but empirical design and optimization of the corresponding probes can be blind and time-consuming. Herein, we report a strategy combining experimental studies with molecular simulation techniques for the rapid and rational design of sensitive fluorescent DNA probes for a representative DNA repair enzyme human apurinic/apyrimidinic endonuclease 1 (APE1). Extended-system Adaptive Biasing Force (eABF) was applied to study the interaction mechanism between DNA probes with respect to the enzyme, based on which a novel sensitive DNA probe was designed efficiently and economically. Product inhibition effect which significantly limited the sensitivity of existing probes was eliminated by decreasing the key interactions between DNA probe products and enzyme. Experimental mechanism studies showed the existence of intramolecular hairpin structure in DNA probes is important for the recognition of APE1 and elimination of product inhibition, which is in consistent with the simulations. The obtained fluorescent DNA nanoprobe (Nanoprobe N) showed a high sensitivity for APE1 with the detection limit as low as 0.5 U/L (∼0.018 pM), and the Nanoprobe N could effectively respond to the variation of APE1 within cells and distinguish cancer cells from normal cells. This work not only demonstrated the effectiveness of molecular simulations in probe design, but also provided a reliable platform for accurate imaging of APE1 and effectors screening at single-cell level.

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A challenge for sensors targeting specific enzymes of interest in their native environment for direct imaging is that they rationally exploit a highly selective fluorescent probe with a high binding affinity to provide real-time detection. Immunohistochemical staining, proteomic analysis, or recent enzymatic fluorescent probes are not optimal for tracking specific enzymes directly in living cells. Herein, we introduce the concept of designing a highly effective fluorescent probe (BVQ1814) targeting phosphodiesterase 10A with a highly potent affinity and a >1000-fold subfamily selectivity by gaining insights into the three-dimensional structural information of the active site of the catalytic pocket. BVQ1814 showed an outstanding binding affinity for PDE10A in vitro and specifically detected PDE10A in living cells, indicating that most PDE10A was probably distributed in the lysosomes. We validated the PDE10A distribution in stable mCherry-PDE10A-overexpressing HepG2 cells. This probe delineated the profile of PDE10A in tissue sections and exhibited a remarkable therapeutic effect as a PDE10A inhibitor for treating pulmonary arterial hypertension. This concept will open up a new avenue for designing a highly effective fluorescent probe for tracking receptor proteins by taking full advantage of the structural information in the ligand-binding pocket of the target of interest.

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The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

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