Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

The novel coronavirus disease, caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), rapidly spreading around the world, poses a major threat to the global public health. Herein, we demonstrated the binding mechanism of PF-07321332, α-ketoamide, lopinavir, and ritonavir to the coronavirus 3-chymotrypsin-like-protease (3CL^pro) by means of docking and molecular dynamic (MD) simulations. The analysis of MD trajectories of 3CL^pro with PF-07321332, α-ketoamide, lopinavir, and ritonavir revealed that 3CL^pro–PF-07321332 and 3CL^pro–α-ketoamide complexes remained stable compared with 3CL^pro–ritonavir and 3CL^pro–lopinavir. Investigating the dynamic behavior of ligand–protein interaction, ligands PF-07321332 and α-ketoamide showed stronger bonding via making interactions with catalytic dyad residues His41–Cys145 of 3CL^pro. Lopinavir and ritonavir were unable to disrupt the catalytic dyad, as illustrated by increased bond length during the MD simulation. To decipher the ligand binding mode and affinity, ligand interactions with SARS-CoV-2 proteases and binding energy were calculated. The binding energy of the bespoke antiviral PF-07321332 clinical candidate was two times higher than that of α-ketoamide and three times than that of lopinavir and ritonavir. Our study elucidated in detail the binding mechanism of the potent PF-07321332 to 3CL^pro along with the low potency of lopinavir and ritonavir due to weak binding affinity demonstrated by the binding energy data. This study will be helpful for the development and optimization of more specific compounds to combat coronavirus disease.

Marine flora is taxonomically diverse, biologically active, and chemically unique. It is an excellent resource, which offers great opportunities for the discovery of new biopharmaceuticals such as immunomodulators and drugs targeting cancerous, inflammatory, microbial, and fungal diseases. The ability of some marine molecules to mediate specific inhibitory activities has been demonstrated in a range of cellular processes, including apoptosis, angiogenesis, and cell migration and adhesion. Immunomodulators have been shown to have significant therapeutic effects on immune-mediated diseases, but the search for safe and effective immunotherapies for other diseases such as sinusitis, atopic dermatitis, rheumatoid arthritis, asthma and allergies is ongoing. This review focuses on the marine-originated bioactive molecules with immunomodulatory potential, with a particular focus on the molecular mechanisms of specific agents with respect to their targets. It also addresses the commercial utilization of these compounds for possible drug improvement using metabolic engineering and genomics.

Toll-like receptor (TLR) signaling plays a critical role in the induction and progression of autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematous, experimental autoimmune encephalitis, type 1 diabetes mellitus and neurodegenerative diseases. Deciphering antigen recognition by antibodies provides insights and defines the mechanism of action into the progression of immune responses. Multiple strategies, including phage display and hybridoma technologies, have been used to enhance the affinity of antibodies for their respective epitopes. Here, we investigate the TLR4 antibody-binding epitope by computational-driven approach. We demonstrate that three important residues, i.e., Y328, N329, and K349 of TLR4 antibody binding epitope identified upon in silico mutagenesis, affect not only the interaction and binding affinity of antibody but also influence the structural integrity of TLR4. Furthermore, we predict a novel epitope at the TLR4-MD2 interface which can be targeted and explored for therapeutic antibodies and small molecules. This technique provides an in-depth insight into antibody–antigen interactions at the resolution and will be beneficial for the development of new monoclonal antibodies. Computational techniques, if coupled with experimental methods, will shorten the duration of rational design and development of antibody therapeutics.

Host inflammatory responses are key to protection against injury; however, persistent inflammation is detrimental and contributes to morbidity and mortality. Herein, we demonstrated the anti-inflammatory role of Arteannuin-B (1) and its new spirocyclic-2-isoxazoline derivative JR-9 and their side effects in acute inflammatory condition in vivo using LPS-induced cytokines assay, carrageenan-induced paw edema, acetic acid-induced writhing and tail immersion. The results show that the spirocyclic-2-isoxazoline derivative is a potent anti-inflammatory agent with minimal cell toxicity as compared to Arteannuin-B. In addition, the efficacies of these compounds were also validated by flow cytometric, computational, and histopathological analysis. Our results show that the anti-inflammatory response of JR-9 significantly reduces the ability of mouse macrophages to produce NO, TNF-α, and IL-6 following LPS stimulation. Therefore, JR-9 is a prospective candidate for the development of anti-inflammatory drugs and its molecular mechanism is likely related to the regulation of NF-κB and MAPK signaling pathway.