Current Medicinal Chemistry - Volume 32, Issue 28, 2025

Computational assessment of the energetics of protein-ligand complexes is a challenge in the early stages of drug discovery. Previous comparative studies on computational methods to calculate the binding affinity showed that targeted scoring functions outperform universal models.

The goal here is to review the application of a simple physics-based model to estimate the binding. The focus is on a mass-spring system developed to predict binding affinity against cyclin-dependent kinase.

Publications in PubMed were searched to find mass-spring models to predict binding affinity. Crystal structures of cyclin-dependent kinases found in the protein data bank and two web servers to calculate affinity based on the atomic coordinates were employed.

One recent study showed how a simple physics-based scoring function (named Taba) could contribute to the analysis of protein-ligand interactions. Taba methodology outperforms robust physics-based models implemented in docking programs such as AutoDock4 and Molegro Virtual Docker. Predictive metrics of 27 scoring functions and energy terms highlight the superior performance of the Taba scoring function for cyclin-dependent kinase.

The recent progress of machine learning methods and the availability of these techniques through free libraries boosted the development of more accurate models to address protein-ligand interactions. Combining a naïve mass-spring system with machine-learning techniques generated a targeted scoring function with superior predictive performance to estimate pKi.

The global pandemic caused by the novel SARS-CoV-2 virus underscores the urgent need for therapeutic interventions. Targeting the virus's main protease (Mpro), crucial for viral replication, is a promising strategy.

The current study aims to discover novel inhibitors of Mpro.

The current study identified five natural compounds (myrrhanol B (C1), myrrhanone B (C2), catechin (C3), quercetin (C4), and feralolide (C5) with strong inhibitory potential against Mpro through virtual screening and computational methods, predicting their binding efficiencies and validated it using the in vitro inhibition activity. The selected compound's toxicity was examined using the MTT assay on a human BJ cell line.

Compound C1 exhibited the highest binding affinity, with a docking score of -9.82 kcal/mol and strong hydrogen bond interactions within Mpro's active site. A microscale molecular dynamics simulation confirmed the stability and tight fit of the compounds in the protein's active pocket, showing superior binding interactions. In vitro assays validated their inhibitory effects, with C1 having the most significant potency (IC50 = 2.85 µM). The non-toxic nature of these compounds in human BJ cell lines was also confirmed, advocating their safety profile.

These findings highlight the effectiveness of combining computational and experimental approaches to identify potential lead compounds for SARS-CoV-2, with C1-C5 emerging as promising candidates for further drug development against this virus.

Drug research is a long process, taking more than 10 years and requiring considerable financial resources. Therefore, researchers and industrials aim to reduce time and cost. Thus, they use computational simulations like molecular docking to explore huge databases of compounds and extract the most promising ones for further tests. Structure-based molecular docking is a complex process mixing surface exploration and energy computation to find the minimal free energy of binding corresponding to the best interaction location.

Our work is developed in the ligand-protein context, where ligands are small compounds like drugs. In most cases, no information is known about where on the protein surface the ligand will bind. Thus, the whole protein surface must be explored, which takes a huge amount of time.

We have developed SGPocket (meaning Spherical Graph Pocket), a binding site prediction method. Our method allows us to reduce the explored protein surface using deep learning without any information about a ligand. SGPocket uses the spherical graph convolutional operator working on a spherical relative positioning of amino acids in the protein. Then, a final step of clustering extracts the binding sites.

Tested and compared (with well-known binding site prediction methods) on a hand-made dataset, our method performed well and can reduce the docking computing time.

Thus, SGPocket allows the reduction of the exploration surface in the molecular docking process by restricting the simulation only to the site(s) predicted to be interesting.

Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis.

This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients.

The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures. The “limma” package was used to identify the differentially expressed genes (DEGs). “Clusterprofiler” package was used for function enrichment analysis, and the “ConsensusClusterPlus” package was performed for consensus clustering. Finally, the “GSVA” package and the Cibersort algorithm were used for the immune infiltration analysis.

We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression.

We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

The objective of this study is to identify dual-target inhibitors against EGFR/c-Met through virtual screening, dynamic simulation, and biological activity evaluation. This endeavor is aimed at overcoming the challenge of drug resistance induced by L858R/T790M mutants.

Active structures were gathered to construct sets of drug molecules. Next, property filtering was applied to the drug structures within the compound library. Active compounds were then identified through virtual screening and cluster analysis. Subsequently, we conducted MTT antitumor activity evaluation and kinase inhibition assays for the active compounds to identify the most promising candidates. Furthermore, AO staining and JC-1 assays were performed on the selected compounds. Ultimately, the preferred compounds underwent molecular docking and molecular dynamics simulation with the EGFR and c-Met proteins, respectively.

The IC50 of T13074 was determined as 2.446 μM for EGFR^L858R/T790M kinase and 7.401 nM for c-Met kinase, underscoring its potential in overcoming EGFR^L858R/T790M resistance. Additionally, T13074 exhibited an IC50 of 1.93 μM on the H1975 cell. Results from AO staining and JC-1 assays indicated that T13074 induced tumor cell apoptosis in a concentration-dependent manner. Notably, the binding energy between T13074 and EGFR protein was found to be -90.329 ± 16.680 kJ/mol, while the binding energy with c-Met protein was -139.935 ± 17.414 kJ/mol.

T13074 exhibited outstanding antitumor activity both in vivo and in vitro, indicating its potential utility as a dual-target EGFR/c-Met inhibitor. This suggests its promising role in overcoming EGFR resistance induced by the L858R/T790M mutation.

Non-Nucleoside Reverse Transcriptases Inhibitors (NNRTIs) are among the most extensively studied enzymes for understanding the biology of Human Immunodeficiency Viruses (HIV) and designing inhibitors for managing HIV infections. Indolyl aryl sulfones (IASs), an underexplored class of potent NNRTIs, require further exploration for the development of newer drugs for HIV.

In this context, we synthesized a series of novels by Indolyl Aryl Sulfones with a hydrazone moiety at the carboxylate site of the indole nucleus. A 2D-QSAR model was developed to predict Reverse Transcriptase inhibitory activity against wild-type RT (WT-RT) enzyme.

The model was successfully applied to predict the HIV-1 inhibitory activity of known Indolyl Aryl Sulfones. Considering the reliability, robustness, and reproducibility of the 2D-QSAR model, we made an in silico prediction of the RT inhibition for our synthesized compounds (1-14).

Molecular docking and dynamics simulations established our synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, as effective NNRTIs by stabilizing HIV reverse transcriptase's structure. Binding energy calculations revealed compound 28 as the strongest inhibitor (-43.21 ± 0.09 kcal/mol), followed by 23 (-40.94 ± 0.10 kcal/mol) and 24 (-39.18±0.08 kcal/mol), emphasizing their binding affinity towards HIV reverse transcriptase.

In summary, the synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, demonstrate significant potential as Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) against HIV. These results highlight the promising role of these compounds in developing novel NNRTIs for managing HIV infections.