Ittipat Meewan

Background/Objectives: Pigmented rice is increasingly recognized as a functional food because of its rich phytochemical composition and health-promoting potential. However, local red rice cultivars from the three southern border provinces of Thailand remain insufficiently characterized. This study comparatively evaluated four whole-grain red rice cultivars—Hawm Gra Dang Ngah 59 (HGDN 59), Hawm Mue Lau (HML), Lued Pla Lai (LPL), and Se Bu Kan Tang (SBKT)—for their chemical composition, antioxidant activities, and antidiabetic potential. Methods: Whole-grain rice samples were extracted with 95% ethanol and assessed for extraction yield, total phenolic content, and total flavonoid content. Antioxidant activity was measured using DPPH^•, FRAP, and anti-lipid peroxidation assays, while antidiabetic activity was measured using α-amylase and α-glucosidase inhibition assays. LC-MS/MS-based chemical profiling, pathway classification, PCA-based chemical space analysis, molecular docking against α-glucosidase, and physicochemical/ADMET prediction were also performed. Results: Among the tested cultivars, HGDN 59 showed the most favorable overall profile, with the highest phenolic content, strongest antioxidant activity, and marked α-glucosidase inhibitory activity. LC-MS/MS analysis combined with docking-based screening revealed that HGDN 59 contained several abundant compounds, including ent-Epicatechin-(4α→6)-ent-epicatechin, cinnamtannin A1, apiin, and α-tocotrienol. These compounds exhibited strong binding affinities toward α-glucosidase (−10.7 to −9.6 kcal/mol), comparable to or slightly more favorable than acarbose. ADMET prediction indicated that most polyphenolic compounds exceeded Lipinski’s rule of five, while α-tocotrienol demonstrated favorable absorption property. Conclusions: This is the first study to suggest that HGDN 59 exhibits potential α-glucosidase inhibitory activity in vitro and may serve as a promising functional food candidate for the dietary management of postprandial glycemic response.

Artificial intelligence (AI) helps solve problems in various fields, including drug discovery, through its capability to process and analyze large amounts of data in a short period of time. AI-aided drug screening is less time-consuming and less expensive compared to experimental trial-and-error-based screening. Using an AI-based framework that learns the relationship between the structures of small molecules and their biological properties offers new perspectives on molecular design. This presentation introduces the use of multiple machine learning models to predict biological activities based on molecular representations, such as molecular fingerprints and line notations of molecular structures. This method is highly effective for molecular design across many targets. The accuracy of the models was assessed using error metrics and R-squared values relative to experimental IC50 values. The models were then applied to virtual screening to identify top candidates, and their binding affinities to viral protein targets were validated using molecular modeling techniques.

Epigenetics encompasses dynamic and reversible modifications that regulate gene activity without altering the underlying DNA sequence. Epigenetic processes, including non-coding RNA interactions, and DNA methylation regulate patterns of gene expression by responding to cellular signaling, environmental stimuli, and developmental cues. The balance of histone acetylation is maintained by histone deacetylase (HDAC) and histone acetyltransferase (HAT) activities. Aberrant HDAC upregulation, often seen in cancer cells, disrupts this balance. HDAC inhibitors (HDACi) are thus used in cancer treatment. However, most synthetic HDACis are not specific to HDAC classes or individual members, highlighting the need for highly selective HDAC inhibitors. Machine learning (ML)-driven methods are now recognized as rapid and cost-efficient tools in drug discovery and development, capable of identifying inhibitors solely from SMILES notation, without requiring the 3D ligand structure. Here, we present a novel and interpretable deep learning-based framework, DeepHDAC3i, for accurate in silico identification of HDAC3i using only the SMILES notation. Firstly, we employed five molecular encoding methods, namely CDKExt, KR, KRC, Pubchem, and RDKIT, to extract the biological and structural information in HDAC3i. These molecular representations were then fused to generate multi-view features. Secondly, elastic net was employed to determine the optimal feature subset and enhance prediction performance. Thirdly, a one-dimensional convolutional neural network (1D-CNN) coupled with the optimal feature set was chosen for the construction of the final model. Finally, our framework leveraged the Shapley Additive exPlanation algorithm to disclose the most important features for identifying HDAC3i. On the independent test dataset, DeepHDAC3i achieved an accuracy of 0.965, MCC of 0.930, and AUC of 0.985, which were significantly higher than several conventional machine learning and deep learning models. In addition, upon comparison with the existing methods, DeepHDAC3i secured the best performance with improvements of approximately 4.80, 4.70, 6.50, and 9.50% in accuracy, F1, AUC, and MCC, respectively. Taken together, DeepHDAC3i is superior to other compared models and can be a useful tool for precisely identifying HDAC3i.

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules’ simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.

The Zika virus (ZIKV), a member of the Flaviviridae family, is considered a major health threat causing multiple cases of microcephaly in newborns and Guillain-Barré syndrome in adults. In this study, we targeted a transient, deep, and hydrophobic pocket of the “super-open” conformation of ZIKV NS2B-NS3 protease to overcome the limitations of the active site pocket. After virtual docking screening of approximately seven million compounds against the novel allosteric site, we selected the top six candidates and assessed them in enzymatic assays. Six candidates inhibited ZIKV NS2B-NS3 protease proteolytic activity at low micromolar concentrations. These six compounds, targeting the selected protease pocket conserved in ZIKV, serve as unique drug candidates and open new opportunities for possible treatment against several flavivirus infections.

One inhibitor of the main SARS-CoV-2 protease has been approved recently by the FDA, yet it targets only SARS-CoV-2 main protease (Mpro). Here, we discovered inhibitors containing thiuram disulfide or dithiobis-(thioformate) tested against three key proteases involved in SARS-CoV-2 replication, including Mpro, SARS-CoV-2 papain-like protease (PLpro), and human cathepsin L. The use of thiuram disulfide and dithiobis-(thioformate) covalent inhibitor warheads was inspired by an idea to find a better alternative than disulfiram, an approved treatment for chronic alcoholism that is currently in phase 2 clinical trials against SARS-CoV-2. Our goal was to find more potent inhibitors that target both viral proteases and one essential human protease to reduce the dosage, improve the efficacy, and minimize the adverse effects associated with these agents. We found that compounds coded as RI175, RI173, and RI172 were the most potent inhibitors in an enzymatic assay against SARS-CoV-2 Mpro, SARS-CoV-2 PLpro, and human cathepsin L, with IC₅₀s of 300, 200, and 200 nM, which is about 5-, 19-, and 11-fold more potent than disulfiram, respectively. In addition, RI173 was tested against SARS-CoV-2 in a cell-based and toxicity assay and was shown to have a greater antiviral effect than disulfiram. The identified compounds demonstrated the promising potential of thiuram disulfide or dithiobis-(thioformate) as a reactive functional group in small molecules that could be further developed for treatment of the COVID-19 virus or related variants.

Zika virus (ZIKV) is a member of the Flaviviridae family and is considered a major health threat causing cases of microcephaly in newborns and Guillain-Barré syndrome in adults. Here we targeted a transient deep and hydrophobic pocket of the super-open conformation of ZIKV NS2B-NS3 protease to overcome the limitations of the orthosteric inhibitors. After virtual docking screening of approximately 7 million compounds against the novel allosteric site we selected the top seven candidates and tested them in an enzymatic assay. Six out of seven top candidates selected by the docking screen inhibited ZIKV NS2B-NS3 protease proteolytic activity at low micromolar concentrations, as well as suppressing viral replication. These six compounds, targeting the selected protease pocket conserved in ZIKV as well as several other Flaviviruses, have opened an opportunity for a new kind of drug candidate that might be useful to treat several flaviviral infections.

There are currently no FDA approved inhibitors of SARS-CoV-2 viral proteases with specific treatment for post-exposure of SARS-CoV-2. Here, we discovered inhibitors containing thiuram disulfide or dithiobis-(thioformate) tested against three key proteases in SARS CoV-2 replication including SARS CoV-2 Main Protease (Mpro), SARS CoV-2 Papain Like Protease (PLpro), and human cathepsin L. The use of thiuram disulfide and dithiobis-(thioformate) covalent inhibitor warheads was inspired by disulfiram, a currently prescribed drug commonly used to treat chronic alcoholism that at the present time is in Phase 2 clinical trials against SARS-CoV-2. At the maximal allowed dose, disulfiram is associated with adverse effects. Our goal was to find more potent inhibitors that target both viral proteases and one essential human protease to reduce the dosage and minimize the adverse effects associated with these agents. We found that compounds coded as RI175, JX 06, and RI172 are the most potent inhibitors from an enzymatic assay against SARS-CoV-2 Mpro, SARS-CoV-2 PLpro, and human cathepsin L with IC ₅₀ s of 330, 250 nM, and 190 nM about 4.5, 17, and 11.5-fold more potent than disulfiram, respectively. The identified protease inhibitors in this series were also tested against SARS CoV-2 in a cell-based and toxicity assay and were shown to have similar or greater antiviral effect than disulfiram. The identified triple protease inhibitors and their derivatives are promising candidates for treatment of the Covid-19 virus and related variants.