Journal of Computer-Aided Molecular Design最新文献

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute–solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute–solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ (({Delta }_{fus}{G}_{A}^{ominus })) and mixing the artificially liquid solute into the solvent (({Delta }_{m}{G}_{left(A:Bright)}^{ominus })). In this approach ({Delta }_{fus}{G}_{A}^{ominus }) is predicted using machine learning models, and the ({Delta }_{m}{G}_{left(A:Bright)}^{ominus }) is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding (pIC_{50}) values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor’s active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset’s preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

MurC, D, E, and F are ATP-dependent ligases involved in the stepwise assembly of the tetrapeptide stem of forming peptidoglycan. As highly conserved targets found exclusively in bacterial cells, they are of significant interest for antibacterial drug discovery. In this study, we employed a computer-aided molecular design approach to identify potential inhibitors of MurF. A biochemical inhibition assay was conducted, screening twenty-four flavonoids and related compounds against MurC-F, resulting in the identification of quercitrin, myricetin, and (–)-epicatechin as MurF inhibitors with IC₅₀ values of 143 µM, 139 µM, and 92 µM, respectively. Notably, (–)-epicatechin demonstrated mixed type inhibition with ATP and uncompetitive inhibition with d-Ala-d-Ala dipeptide and UM3DAP substrates. Furthermore, in silico analysis using Sitemap and subsequent docking analysis using Glide revealed two plausible binding sites for (–)-epicatechin. The study also investigated the crucial structural features required for activity, with a particular focus on the substitution pattern and hydroxyl group positions, which were found to be important for the activity. The study highlights the significance of computational approaches in targeting essential enzymes involved in bacterial peptidoglycan synthesis.

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Owing to their potential to cause serious adverse health effects, significant efforts have been made to develop antidotes for organophosphate (OP) anticholinesterases, such as nerve agents. To be optimally effective, antidotes must not only reactivate inhibited target enzymes, but also have the ability to cross the blood–brain barrier (BBB). Progress has been made toward brain-penetrating acetylcholinesterase reactivators through the development of a new group of substituted phenoxyalkyl pyridinium oximes. To help in the selection and prioritization of compounds for future synthesis and testing within this class of chemicals, and to identify candidate broad-spectrum molecules, an in silico framework was developed to systematically generate structures and screen them for reactivation efficacy and BBB penetration potential.

Herein, we conducted simulations of trimeric Spike from several SARS-CoV-2 variants of concern (Delta and Omicron sub-variants BA.2, BA.5, and BQ.1) and investigated the mechanisms by which specific mutations confer resistance to neutralizing antibodies. We observed that the mutations primarily affect the cooperation between protein domains within and between protomers. The substitutions K417N and L452R expand hydrogen bonding interactions, reducing their interaction with neutralizing antibodies. By interacting with nearby residues, the K444T and N460K mutations in the Spike^BQ.1 variant potentially reduces solvent exposure, thereby promoting resistance to antibodies. We also examined the impact of D614G, P681R, and P681H substitutions on Spike protein structure that may be related to infectivity. The D614G substitution influences communication between a glycine residue and neighboring domains, affecting the transition between up- and -down RBD states. The P681R mutation, found in the Delta variant, enhances correlations between protein subunits, while the P681H mutation in Omicron sub-variants weakens long-range interactions that may be associated with reduced fusogenicity. Using a multiple linear regression model, we established a connection between inter-protomer communication and loss of sensitivity to neutralizing antibodies. Our findings underscore the importance of structural communication between protein domains and provide insights into potential mechanisms of immune evasion by SARS-CoV-2. Overall, this study deepens our understanding of how specific mutations impact SARS-CoV-2 infectivity and shed light on how the virus evades the immune system.

Graphical abstract

Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

Polarization and charge transfer strongly characterize the ligand-receptor interaction when metal atoms are present, as for the Au(I)-biscarbene/DNA G-quadruplex complexes. In a previous work (J Comput Aided Mol Des2022, 36, 851–866) we used the ab initio FMO2 method at the RI-MP2/6-31G* level of theory with the PCM [1] solvation approach to calculate the binding energy (ΔE^FMO) of two Au(I)-biscarbene derivatives, [Au(9-methylcaffein-8-ylidene)₂]⁺ and [Au(1,3-dimethylbenzimidazole-2-ylidene)₂]⁺, able to interact with DNA G-quadruplex motif. We found that ΔE^FMO and ligand-receptor pair interaction energies (E^INT) show very large negative values making the direct comparison with experimental data difficult and related this issue to the overestimation of the embedded charge transfer energy between fragments containing metal atoms. In this work, to improve the accuracy of the FMO method for predicting the binding affinity of metal-based ligands interacting with DNA G-quadruplex (Gq), we assess the effect of the following computational features: (i) the electron correlation, considering the Hartree–Fock (HF) and a post-HF method, namely RI-MP2; (ii) the two (FMO2) and three-body (FMO3) approaches; (iii) the basis set size (polarization functions and double-ζ vs. triple-ζ) and (iv) the embedding electrostatic potential (ESP). Moreover, the partial screening method was systematically adopted to simulate the solvent screening effect for each calculation. We found that the use of the ESP computed using the screened point charges for all atoms (ESP-SPTC) has a critical impact on the accuracy of both ΔE^FMO and E^INT, eliminating the overestimation of charge transfer energy and leading to energy values with magnitude comparable with typical experimental binding energies. With this computational approach, E^INT values describe the binding efficiency of metal-based binders to DNA Gq more accurately than ΔE^FMO. Therefore, to study the binding process of metal containing systems with the FMO method, the adoption of partial screening solvent method combined with ESP-SPCT should be considered. This computational protocol is suggested for FMO calculations on biological systems containing metals, especially when the adoption of the default ESP treatment leads to questionable results.