Structure-based virtual screening utilizes molecular docking to explore and analyze ligand–macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule–macromolecule and 12 peptide–macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson’s and Spearman’s correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance.

The “Long-COVID syndrome” has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<−6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.

Intranasal diamorphine (IND), approved for managing breakthrough pain in the UK, has been identified as an acceptable alternative offering effective, expedient, and less traumatic analgesia for children. However, the current dose regimen in pediatric populations relies on clinical expertise while the pharmacokinetics properties are poorly understood. This study aimed to develop diamorphine population pharmacokinetics (pop‐PK) models and simulate the IND dosing in virtual pediatric subjects. An integrated four‐compartment pop‐PK model with first‐order absorption and elimination provided an appropriate fit and characterized publicly available 385 concentration measurements of diamorphine, 6‐monoacetylmorphine, and morphine collected from adults. Body weight allometry and renal function maturation (age) were incorporated into the final model, serving as two covariates. The estimated IND relative bioavailability was around 52% compared with intramuscularly injected diamorphine. Using this final model, the morphine plasma concentrations, as the active metabolite for pain relief, were simulated in virtual subjects. The utility of model extrapolation was supported by external verification with acceptable average fold errors of 1.06 ± 0.30 and 0.83 ± 0.07 for morphine maximum concentration and exposures. Meanwhile, the simulated morphine concentration–time profiles could recover the PK profiles observed in children after a single dose of IND. The model‐based dosing simulations were therefore assessed in four children age groups to match the therapeutic window of morphine concentrations in steady state (10–20 μg/L). Our study demonstrates that the dose regimen of 0.3 mg/kg loading dose plus 0.1 mg/kg hourly maintenance dose is generally appropriate for multiple pediatric populations with breakthrough pain, in the view of PK.

Background: The emergence of multidrug-resistant bacteria, coupled with the stagnation of antibiotic discovery, underscores the urgent need for new therapeutic strategies targeting underexplored bacterial mechanisms. The Escherichia coli mechanosensitive channel of large conductance (Ec-MscL) is a highly conserved bacterial membrane protein that regulates osmotic homeostasis through gated pore formation; however, its fully open conformation remains structurally unresolved due to experimental limitations. Building upon our previous studies, in which an external electric field (EEF) was used to accelerate the translocation of the antibiotic dihydrostreptomycin (DHS) through Ec-MscL, we extend this approach to investigate peptide-scale gating transitions and identify the partially open conformation of this channel for subsequent binder design. Methods: In this work, we employed EEF-steered molecular dynamics (MD) simulations to model the translocation of the experimentally validated permeant bovine pancreatic trypsin inhibitor (BPTI), a small positively charged peptide, through Ec-MscL. This approach enabled the capture and stabilization of an a partially open-channel conformation of Ec-MscL, which was subsequently evaluated using QMEAN structural quality assessment and MM/PBSA free-energy calculations. Using this open-state structure as a template, we performed virtual screening against a curated antibiotic library from the Community for Open Antimicrobial Drug Discovery (CO-ADD), followed by conventional MD simulations of the top-ranked candidates. Results: Eight ligands were computationally identified that stably interact with the channel and were predicted to inhibit its closure, highlighting their potential as antibiotics or antibiotic adjuvants. In addition, four commercially available compounds were evaluated in cell viability assays, with the results suggesting that compound abJ0y affects bacterial growth. Conclusions: Together, our study establishes a structure-guided workflow for identifying functional states of mechanosensitive channels and presents a computational pipeline for antibiotic discovery, which was further supported by the identification of a compound that reduced growth in MscL-expressing cells with potential antibacterial activity.

By mining crystallographic proton positions, we trained a graph neural network to predict stable tautomeric states directly from 2D molecular topology, enabling rapid tautomer assignment for high-throughput molecular discovery.