Isatin-based virtual high throughput screening, molecular docking, DFT, QM/MM, MD and MM-PBSA study of novel inhibitors of SARS-CoV-2 main protease

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

Cheminformatics and systems pharmacology approaches to unveil the potential plant bioactives to combat COVID-19

COVID-19 was a global pandemic in the year 2020. Several treatment options failed to cure the disease. Thus, plant-based medicines are becoming a trend nowadays due to their less side effects. Bioactive chemicals from natural sources have been utilised for centuries as treatment options for a variety of ailments. To find out the potent bioactive compounds to counteract COVID-19, we use systems pharmacology and cheminformatics. They use the definitive data and predict the possible outcomes. In this study, we collected a total of 72 phytocompounds from the medicinally important plants such as Garcinia mangostana and Cinnamomum verum, of which 13 potential phytocompounds were identified to be active against the COVID-19 infection based on Swiss Target Prediction and compound target network analysis. These phytocompounds were annotated to identify the specific human receptor that targets COVID-19-specific genes such as MAPK8, MAPK14, ACE, CYP3A4, TLR4 and TYK2. Among these, compounds such as smeathxanthone A, demethylcalabaxanthone, mangostanol, trapezifolixanthone from Garcinia mangostana and camphene from C. verum were putatively target various COVID-19-related genes. Molecular docking results showed that smeathxanthone A and demethylcalabaxanthone exhibit increased binding efficiency towards the COVID-19-related receptor proteins. These compounds also showed efficient putative pharmacoactive properties than the commercial drugs ((R)-remdesivir, favipiravir and hydroxychloroquine) used to cure COVID-19. In conclusion, our study highlights the use of cheminformatics approach to unravel the potent and novel phytocompounds against COVID-19. These phytocompounds may be safer to use, more efficient and less harmful. This study highlights the value of natural products in the search for new drugs and identifies candidates with great promise.

Indole-Based Compounds as Potential Drug Candidates for SARS-CoV-2

The COVID-19 pandemic has posed a significant threat to society in recent times, endangering human health, life, and economic well-being. The disease quickly spreads due to the highly infectious SARS-CoV-2 virus, which has undergone numerous mutations. Despite intense research efforts by the scientific community since its emergence in 2019, no effective therapeutics have been discovered yet. While some repurposed drugs have been used to control the global outbreak and save lives, none have proven universally effective, particularly for severely infected patients. Although the spread of the disease is generally under control, anti-SARS-CoV-2 agents are still needed to combat current and future infections. This study reviews some of the most promising repurposed drugs containing indolyl heterocycle, which is an essential scaffold of many alkaloids with diverse bio-properties in various biological fields. The study also discusses natural and synthetic indole-containing compounds with anti-SARS-CoV-2 properties and computer-aided drug design (in silico studies) for optimizing anti-SARS-CoV-2 hits/leads.

AI-Driven Discovery of SARS-CoV-2 Main Protease Fragment-like Inhibitors with Antiviral Activity In Vitro

SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved into 16 nonstructural proteins (nsp) by two viral cysteine proteases encoded within the polyproteins. The main protease (Mpro, nsp5) cleaves the majority of the nsp's, making it essential for viral replication and has been successfully targeted for the development of antivirals. The first oral Mpro inhibitor, nirmatrelvir, was approved for treatment of COVID-19 in late December 2021 in combination with ritonavir as Paxlovid. Increasing the arsenal of antivirals and development of protease inhibitors and other antivirals with a varied mode of action remains a priority to reduce the likelihood for resistance emerging. Here, we report results from an artificial intelligence-driven approach followed by in vitro validation, allowing the identification of five fragment-like Mpro inhibitors with IC50 values ranging from 1.5 to 241 μM. The three most potent molecules (compounds 818, 737, and 183) were tested against SARS-CoV-2 by in vitro replication in Vero E6 and Calu-3 cells. Compound 818 was active in both cell models with an EC50 value comparable to its measured IC50 value. On the other hand, compounds 737 and 183 were only active in Calu-3, a preclinical model of respiratory cells, showing selective indexes twice as high as those for compound 818. We also show that our in silico methodology was successful in identifying both reversible and covalent inhibitors. For instance, compound 818 is a reversible chloromethylamide analogue of 8-methyl-γ-carboline, while compound 737 is an N-pyridyl-isatin that covalently inhibits Mpro. Given the small molecular weights of these fragments, their high binding efficiency in vitro and efficacy in blocking viral replication, these compounds represent good starting points for the development of potent lead molecules targeting the Mpro of SARS-CoV-2.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Potential of Natural Alkaloids From Jadwar (Delphinium denudatum) as Inhibitors Against Main Protease of COVID-19: A Molecular Modeling Approach

The ongoing pandemic coronavirus disease (COVID-19) caused by a novel corona virus, namely, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has had a major impact on global public health. COVID-19 cases continue to increase across the globe with high mortality rates in immunocompromised patients. There is still a pressing demand for drug discovery and vaccine development against this highly contagious disease. To design and develop antiviral drugs against COVID-19, the main protease (M^pro) has emerged as one of the important drug targets. In this context, the present work explored Jadwar (Delphinium denudatum)-derived natural alkaloids as potential inhibitors against M^pro of SARS-CoV-2 by employing a combination of molecular docking and molecular dynamic simulation-based methods. Molecular docking and interaction profile analysis revealed strong binding on the M^pro functional domain with four natural alkaloids viz. panicutine (-7.4 kcal/mol), vilmorrianone (-7.0 kcal/mol), denudatine (-6.0 kcal/mol), and condelphine (-5.9 kcal/mol). The molecular docking results evaluated by using the MD simulations on 200 nanoseconds confirmed highly stable interactions of these compounds with the M^pro. Additionally, mechanics/generalized Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations also affirmed the docking results. Natural alkaloids explored in the present study possess the essential drug-likeness properties, namely, absorption, distribution, metabolism, and excretion (ADME), and are in accordance with Lipinski's rule of five. The results of this study suggest that these four bioactive molecules, namely, condelphine, denudatine, panicutine, and vilmorrianone, might be effective candidates against COVID-19 and can be further investigated using a number of experimental methods.

Inhibition Potencies of Phytochemicals Derived from Sesame Against SARS-CoV-2 Main Protease: A Molecular Docking and Simulation Study

The ongoing COVID-19 pandemic, caused by SARS-CoV-2, has now spread across the nations with high mortality rates and multifaceted impact on human life. The proper treatment methods to overcome this contagious disease are still limited. The main protease enzyme (Mpro, also called 3CLpro) is essential for viral replication and has been considered as one of the potent drug targets for treating COVID-19. In this study, virtual screening was performed to find out the molecular interactions between 36 natural compounds derived from sesame and the Mpro of COVID-19. Four natural metabolites, namely, sesamin, sesaminol, sesamolin, and sesamolinol have been ranked as the top interacting molecules to Mpro based on the affinity of molecular docking. Moreover, stability of these four sesame-specific natural compounds has also been evaluated using molecular dynamics (MD) simulations for 200 nanoseconds. The molecular dynamics simulations and free energy calculations revealed that these compounds have stable and favorable energies, causing strong binding with Mpro. These screened natural metabolites also meet the essential conditions for drug likeness such as absorption, distribution, metabolism, and excretion (ADME) properties as well as Lipinski's rule of five. Our finding suggests that these screened natural compounds may be evolved as promising therapeutics against COVID-19.