Development and experimental validation of computational methods for human antibody affinity enhancement.

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2 nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components.

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

"Blade of Polarized Water Molecule" Is the Key to Hydrolase Catalysis Regulation.

Hydrolysis catalyzed by aspartic proteases is a crucial reaction in many biological processes. However, anchoring water molecules and unifying multiple catalytic pathways remain significant challenges. Consequently, molecular design often compromises by focusing on enhancing substrate specificity. Using our self-developed polarizable point charge (PPC) force field, we determined the significant role of polarization in the hydrolase of pepsin for the first time. To be stably anchored in the active site, the water should be intensely polarized with a charge higher than -0.94e. Induced by this polarization, the pepsin was shown to support three general base/general acid pathways, with a preference for the gemdiol-intermediate-based pathway. Consequently, we proposed the "Blade of Polarized Water Molecule" model for rational enzyme design, highlighting that the polarization of both the attacking water and the attacked carbonyl is crucial for enhancing hydrolysis. Mutants D290Q and S172P showed activity enhancements of 191.23% and 324.70%, respectively. The improved polarization of water, carbonyl, and relevant nucleophilic attack distances in the mutants reaffirmed the crucial role of polarization in improving hydrolysis. This study provides a new perspective on hydrolase analysis and modification.

Substituent effects on the selectivity of ambimodal [6+4]/[4+2] cycloaddition.

In this work, we report a density functional theory (DFT) study and a dynamical trajectory study of substituent effects on the ambimodal [6+4]/[4+2] cycloaddition proposed for 1,3,5,10,12-cycloheptadecapentaene, referred to as cycloheptadecapentaene. The proposed cycloaddition proceeds through an ambimodal transition state, which results in both a [6+4] adduct a [4+2] adduct directly. The [6+4] adduct can be readily converted to the [4+2] adduct via a Cope rearrangement. We study the selectivity of the reaction with regard to the position of substituents, steric effects of substituents, and electronic effects of substituents. In the dynamical trajectory study, we find that nitro-substituted reactants lead to a new product from the ambimodal transition state via the hetero Diels-Alder reaction, and this new product can then be converted to a [4+2] adduct by a hetero [3, 3]-sigmatropic rearrangement. These results may provide insights for designing more bridged heterocyclic compounds.

Biosynthesis of eriodictyol in citrus waster by endowing P450BM3 activity of naringenin hydroxylation.

The flavonoid naringenin is abundantly present in pomelo peels, and the unprocessed naringenin in wastes is not friendly for the environment once discarded directly. Fortunately, the hydroxylated product of eriodictyol from naringenin exhibits remarkable antioxidant and anticancer properties. The P450s was suggested promising for the bioconversion of the flavonoids, but less naturally existed P450s show hydroxylation activity to C3' of the naringenin. By well analyzing the catalytic mechanism and the conformations of the naringenin in P450, we proposed that the intermediate Cmpd I ((porphyrin)Fe = O) is more reasonable as key conformation for the hydrolyzation, and the distance between C3'/C5' of naringenin to the O atom of CmpdI determines the hydroxylating activity for the naringenin. Thus, the "flying kite model" that gradually drags the C-H bond of the substrate to the O atom of CmpdI was put forward for rational design. With ab initio design, we successfully endowed the self-sufficient P450-BM3 hydroxylic activity to naringenin and obtained mutant M5-5, with kcat, Km, and kcat/Km values of 230.45 min-1, 310.48 µM, and 0.742 min-1 µM-1, respectively. Furthermore, the mutant M4186 was screened with kcat/Km of 4.28-fold highly improved than the reported M13. The M4186 also exhibited 62.57% yield of eriodictyol, more suitable for the industrial application. This study provided a theoretical guide for the rational design of P450s to the nonnative compounds. KEY POINTS: •The compound I is proposed as the starting point for the rational design of the P450BM3 •"Flying kite model" is proposed based on the distance between O of Cmpd I and C3'/C5' of naringenin •Mutant M15-5 with 1.6-fold of activity than M13 was obtained by ab initio modification.

Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease.

Papain-like protease (PLpro), a non-structural protein encoded by SARS-CoV-2, is an important therapeutic target. Regions 1 and 5 of an existing drug, GRL0617, can be optimized to produce cooperativity with PLpro binding, resulting in stronger binding affinity. This work investigated the origin of the cooperativity using molecular dynamics simulations combined with the interaction entropy (IE) method. The regions' improvement exhibits cooperativity by calculating the binding free energies between the complex of PLpro-inhibitor. The thermodynamic integration method further verified the cooperativity generated in the drug improvement. To further determine the specific source of cooperativity, enthalpy and entropy in the complexes were calculated using molecular mechanics/generalized Born surface area and IE. The results show that the entropic change is an important contributor to the cooperativity. Our study also identified residues P248, Q269, and T301 that play a significant role in cooperativity. The optimization of the inhibitor stabilizes these residues and minimizes the entropic loss, and the cooperativity observed in the binding free energy can be attributed to the change in the entropic contribution of these residues. Based on our research, the application of cooperativity can facilitate drug optimization, and provide theoretical ideas for drug development that leverage cooperativity by reducing the contribution of entropy through multi-locus binding.

Identification and Validation of New DNA-PKcs Inhibitors through High-Throughput Virtual Screening and Experimental Verification.

DNA-PKcs is a crucial protein target involved in DNA repair and response pathways, with its abnormal activity closely associated with the occurrence and progression of various cancers. In this study, we employed a deep learning-based screening and molecular dynamics (MD) simulation-based pipeline, identifying eight candidates for DNA-PKcs targets. Subsequent experiments revealed the effective inhibition of DNA-PKcs-mediated cell proliferation by three small molecules (5025-0002, M769-1095, and V008-1080). These molecules exhibited anticancer activity with IC50 (inhibitory concentration at 50%) values of 152.6 µM, 30.71 µM, and 74.84 µM, respectively. Notably, V008-1080 enhanced homology-directed repair (HDR) mediated by CRISPR/Cas9 while inhibiting non-homologous end joining (NHEJ) efficiency. Further investigations into the structure-activity relationships unveiled the binding sites and critical interactions between these small molecules and DNA-PKcs. This is the first application of DeepBindGCN_RG in a real drug screening task, and the successful discovery of a novel DNA-PKcs inhibitor demonstrates its efficiency as a core component in the screening pipeline. Moreover, this study provides important insights for exploring novel anticancer therapeutics and advancing the development of gene editing techniques by targeting DNA-PKcs.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1.

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Rational Design of a Highly Specific Prolyl Endopeptidase To Activate the Antihypertensive Effect of Peptides.

Enzymatic hydrolysis of food-derived proteins to produce bioactive peptides could activate food functions such as antihypertension. However, the diversity of enzymatic hydrolysis products can reduce bioactive peptides' efficacy. Highly specific proteases can homogenize the hydrolysis products to reduce the production of impotent peptides. In this study, we successfully obtained M. xanthus prolyl endopeptidase mutant Y451M by constraint/free molecular dynamics simulations and binding energy calculations. The specificity of Y451M for proline was increased by 286 % compared to WT, while its activity was almost unchanged. Milk-derived substrates processed with Y451M showed an antihypertensive effect that was 567 % higher than without enzymes. The ability to activate food antihypertension increased 152 % and the use of enzyme by 192 % compared with WT. Specific proteases are thus valuable tools in the processing of complex substrates to obtain bioactive peptides.

Combined Antibodies Evusheld against the SARS-CoV-2 Omicron Variants BA.1.1 and BA.5: Immune Escape Mechanism from Molecular Simulation.

The Omicron lineage of SARS-CoV-2, which was first reported in November 2021, has spread globally and become dominant, splitting into several sublineages. Experiments have shown that Omicron lineage has escaped or reduced the activity of existing monoclonal antibodies, but the origin of escape mechanism caused by mutation is still unknown. This work uses molecular dynamics and umbrella sampling methods to reveal the escape mechanism of BA.1.1 to monoclonal antibody (mAb) Tixagevimab (AZD1061) and BA.5 to mAb Cilgavimab (AZD8895), both mAbs were combined to form antibody cocktail, Evusheld (AZD7442). The binding free energy of BA.1.1-AZD1061 and BA.5-AZD8895 has been severely reduced due to multiple-site mutated Omicron variants. Our results show that the two Omicron variants, which introduce a substantial number of positively charged residues, can weaken the electrostatic attraction between the receptor binding domain (RBD) and AZD7442, thus leading to a decrease in affinity. Additionally, using umbrella sampling along dissociation pathway, we found that the two Omicron variants severely impaired the interaction between the RBD of SARS-CoV-2's spike glycoprotein (S protein) and complementary determining regions (CDRs) of mAbs, especially in CDR3H. Although mAbs AZD8895 and AZD1061 are knocked out by BA.5 and BA.1.1, respectively, our results confirm that the antibody cocktail AZD7442 retains activity against BA.1.1 and BA.5 because another antibody is still on guard. The study provides theoretical insights for mAbs interacting with BA.1.1 and BA.5 from both energetic and dynamic perspectives, and we hope this will help in developing new monoclonals and combinations to protect those unable to mount adequate vaccine responses.

Screening Power of End-Point Free-Energy Calculations in Cucurbituril Host-Guest Systems.

End-point free-energy methods as an indispensable component in virtual screening are commonly recognized as a tool with a certain level of screening power in pharmaceutical research. While a huge number of records could be found for end-point applications in protein-ligand, protein-protein, and protein-DNA complexes from academic and industrial reports, up to now, there is no large-scale benchmark in host-guest complexes supporting the screening power of end-point free-energy techniques. A good benchmark requires a data set of sufficient coverage of pharmaceutically relevant chemical space, a long-time sampling length supporting the trajectory approximation of the ensemble average, and a sufficient sample size of receptor-acceptor pairs to stabilize the performance statistics. In this work, selecting a popular family of macrocyclic hosts named cucurbiturils, we construct a large data set containing 154 host-guest pairs, perform extensive end-point sampling of several hundred nanosecond lengths for each system, and extract the free-energy estimates with a variety of end-point free-energy techniques, including the advanced three-trajectory dielectric-constant-variable regime proposed in our recent work. The best-performing end-point protocol employs GAFF2 for solute descriptions, the three-trajectory end-point sampling regime, and the MM/GBSA Hamiltonian in free-energy extraction, achieving a high ranking metrics of Kendall τ > 0.6, a Pearlman predictive index of ∼0.8, and a high scoring power of Pearson r > 0.8. The current project as the first large-scale systematic benchmark of end-point methods in host-guest complexes in academic publications provides solid evidence of the applicability of end-point techniques and direct guidance of computational setups in practical host-guest systems.

MolTaut: A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution.

Fast and proper treatment of the tautomeric states for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomeric states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic pKa. Our test shows that the ranking ability of the AI-based tautomer scoring approach is comparable to the DFT method (wB97X/6-31G*//M062X/6-31G*/SMD) from which the AI models try to learn. We find that the substitution effect on tautomeric equilibrium is well predicted by MolTaut, which is helpful in computer-aided ligand design. The source code of MolTaut is freely available to researchers and can be accessed at https://github.com/xundrug/moltaut. To facilitate the usage of MolTaut by medicinal chemists, we made a free web server, which is available at http://moltaut.xundrug.cn. MolTaut is a handy tool for investigating the tautomerization issue in drug discovery.

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Binding of berberine derivates to G-quadruplex: insight from a computational study.

Human telomerase exhibits significant activity in cancer cells relative to normal cells, which contributes to the immortal proliferation of cancer cells. To counter this, the stabilization of G-quadruplexes formed in the guanine-rich sequence of the cancer cell chromosome has emerged as a promising avenue for anti-cancer therapy. Berberine (BER), an alkaloid that is derived from traditional Chinese medicines, has shown potential for stabilizing G-quadruplexes. To investigate the atomic interactions between G-quadruplexes and BER and its derivatives, molecular dynamics simulations were conducted. Modeling the interactions between G-quadruplexes and ligands accurately is challenging due to the strong negative charge of nucleic acids. Thus, various force fields and charge models for the G-quadruplex and ligands were tested to obtain precise simulation results. The binding energies were calculated by a combination of molecular mechanics/generalized Born surface area and interaction entropy methods, and the calculated results correlated well with experimental results. B-factor and hydrogen bond analyses demonstrated that the G-quadruplex was more stable in the presence of ligands than in the absence of ligands. Calculation of the binding free energy showed that the BER derivatives bind to a G-quadruplex with higher affinity than that of BER. The breakdown of the binding free energy to per-nucleotide energies suggested that the first G-tetrad played a primary role in binding. Additionally, energy and geometric properties analyses indicated that van der Waals interactions were the most favorable interactions between the derivatives and the G-quadruplexes. Overall, these findings provide crucial atomic-level insights into the binding of G-quadruplexes and their inhibitors.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-Guest Binding: III. Force-Field Comparison, Three-Trajectory Realization and Further Dielectric Augmentation.

Host-guest binding, despite the relatively simple structural and chemical features of individual components, still poses a challenge in computational modelling. The extreme underperformance of standard end-point methods in host-guest binding makes them practically useless. In the current work, we explore a potentially promising modification of the three-trajectory realization. The alteration couples the binding-induced structural reorganization into free energy estimation and suffers from dramatic fluctuations in internal energies in protein-ligand situations. Fortunately, the relatively small size of host-guest systems minimizes the magnitude of internal fluctuations and makes the three-trajectory realization practically suitable. Due to the incorporation of intra-molecular interactions in free energy estimation, a strong dependence on the force field parameters could be incurred. Thus, a term-specific investigation of transferable GAFF derivatives is presented, and noticeable differences in many aspects are identified between commonly applied GAFF and GAFF2. These force-field differences lead to different dynamic behaviors of the macrocyclic host, which ultimately would influence the end-point sampling and binding thermodynamics. Therefore, the three-trajectory end-point free energy calculations are performed with both GAFF versions. Additionally, due to the noticeable differences between host dynamics under GAFF and GAFF2, we add additional benchmarks of the single-trajectory end-point calculations. When only the ranks of binding affinities are pursued, the three-trajectory realization performs very well, comparable to and even better than the regressed PBSA_E scoring function and the dielectric constant-variable regime. With the GAFF parameter set, the TIP3P water in explicit solvent sampling and either PB or GB implicit solvent model in free energy estimation, the predictive power of the three-trajectory realization in ranking calculations surpasses all existing end-point methods on this dataset. We further combine the three-trajectory realization with another promising modified end-point regime of varying the interior dielectric constant. The combined regime does not incur sizable improvements for ranks and deviations from experiment exhibit non-monotonic variations.

HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models.

The human ether-à-go-go-related gene (hERG) K+ channel plays an important role in cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome (LQTS) and even sudden cardiac death. Due to severe hERG-related cardiotoxicity, many drugs have been withdrawn from the market. Therefore, it is necessary to estimate the chemical blockade of hERG in the early stage of drug discovery. In this study, we collected 12,850 compounds with hERG inhibition data from the literature and trained a series of hERG blocking classification models based on the MACCS and Morgan fingerprints. A consensus model named HergSPred was generated based on the individual models using voting principles. The accuracy of HergSPred is higher than previous models using identical training and test sets. Moreover, we analyzed the contribution of each input fingerprint to the prediction output to obtain intuitive chemical insights into the hERG inhibition, which allows visualization of warning substructures that may cause cardiotoxicity in the input compound. The model is available at http://www.icdrug.com/ICDrug/T.