Evaluating ChatGPT as a self-learning tool in medical biochemistry: A performance assessment in undergraduate medical university examination.

The emergence of ChatGPT as one of the most advanced chatbots and its ability to generate diverse data has given room for numerous discussions worldwide regarding its utility, particularly in advancing medical education and research. This study seeks to assess the performance of ChatGPT in medical biochemistry to evaluate its potential as an effective self-learning tool for medical students. This evaluation was carried out using the university examination question papers of both parts 1 and 2 of medical biochemistry which comprised theory and multiple choice questions (MCQs) accounting for a total of 100 in each part. The questions were used to interact with ChatGPT, and three raters independently reviewed and scored the answers to prevent bias in scoring. We conducted the inter-item correlation matrix and the interclass correlation between raters 1, 2, and 3. For MCQs, symmetric measures in the form of kappa value (a measure of agreement) were performed between raters 1, 2, and 3. ChatGPT generated relevant and appropriate answers to all questions along with explanations for MCQs. ChatGPT has "passed" the medical biochemistry university examination with an average score of 117 out of 200 (58%) in both papers. In Paper 1, ChatGPT has secured 60 ± 2.29 and 57 ± 4.36 in Paper 2. The kappa value for all the cross-analysis of Rater 1, Rater 2, and Rater 3 scores in MCQ was 1.000. The evaluation of ChatGPT as a self-learning tool in medical biochemistry has yielded important insights. While it is encouraging that ChatGPT has demonstrated proficiency in this area, the overall score of 58% indicates that there is work to be done. To unlock its full potential as a self-learning tool, ChatGPT must focus on generating not only accurate but also comprehensive and contextually relevant content.

Repurposing of potential antiviral drugs against RNA-dependent RNA polymerase of SARS-CoV-2 by computational approach.

The high incidences of COVID-19 cases are believed to be associated with high transmissibility rates, which emphasizes the need for the discovery of evidence-based antiviral therapies for curing the disease. The rationale of repurposing existing classes of antiviral small molecule therapeutics against SARS-CoV-2 infection has been expected to accelerate the tedious and expensive drug development process. While Remdesivir has been recently approved to be the first treatment option for specific groups of COVID-19 patients, combinatory therapy with potential antiviral drugs may be necessary to enhance the efficacy in different populations. Hence, a comprehensive list of investigational antimicrobial drug compounds such as Favipiravir, Fidaxomicin, Galidesivir, GC376, Ribavirin, Rifabutin, and Umifenovir were computationally evaluated in this study. We performed in silico docking and molecular dynamics simulation on the selected small molecules against RNA-dependent RNA polymerase, which is one of the key target proteins of SARS-CoV-2, using AutoDock and GROMACS. Interestingly, our results revealed that the macrocyclic antibiotic, Fidaxomicin, possesses the highest binding affinity with the lowest energy value of -8.97 kcal/mol binding to the same active sites of RdRp. GC376, Rifabutin, Umifenovir and Remdesivir were identified as the next best compounds. Therefore, the above-mentioned compounds could be considered good leads for further preclinical and clinical experimentations as potentially efficient antiviral inhibitors for combination therapies against SARS-CoV-2.

Ellagic Acid as a Potential Inhibitor against the Nonstructural Protein NS3 Helicase of Zika Virus: A Molecular Modelling Study.

Zika virus is a member of the Flaviviridae family and genus Flavivirus, which has a phylogenetic relationship with spondweni virus. It spreads to humans through a mosquito bite. To identify potential inhibitors for the Zika virus with biosafety, we selected natural antiviral compounds isolated from plant sources and screened against NS3 helicase of the Zika virus. The enzymatic activity of the NS3 helicase is associated with the C-terminal region and is concerned with RNA synthesis and genome replication. It serves as a crucial target for the Zika virus. We carried out molecular docking for the target NS3 helicase against the selected 25 phytochemicals using AutoDock Vina software. Among the 25 plant compounds, we identified NS3 helicase-ellagic acid (-9.9 kcal/mol), NS3 helicase-hypericin (-9.8 kcal/mol), and NS3 helicase-pentagalloylglucose (-9.5 kcal/mol) as the best binding affinity compounds based on their binding energies. To understand the stability of these complexes, molecular dynamic simulations were executed and the trajectory analysis exposed that the NS3 helicase-ellagic acid complex possesses greater stability than the other two complexes such as NS3 helicase-hypericin and NS3 helicase-pentagalloylglucose. The ADMET property prediction of these compounds resulted in nontoxicity and noncarcinogenicity.