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 integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.

Middle East respiratory syndrome coronavirus (MERS-CoV) causes high fever, cough, acute respiratory tract infection and multiorgan dysfunction that may eventually lead to the death of the infected individuals. MERS-CoV is thought to be transmitted to humans through dromedary camels. The occurrence of the virus was first reported in the Middle East and it subsequently spread to several parts of the world. Since 2012, about 1368 infections, including ~487 deaths, have been reported worldwide. Notably, the recent human-to-human ‘superspreading’ of MERS-CoV in hospitals in South Korea has raised a major global health concern. The fatality rate in MERS-CoV infection is four times higher compared with that of the closely related severe acute respiratory syndrome coronavirus infection. Currently, no drug has been clinically approved to control MERS-CoV infection. In this study, we highlight the potential drug targets that can be used to develop anti-MERS-CoV therapeutics.

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.

Lipopolysaccharide (LPS) is a component of the outer membrane of mainly Gram-negative bacteria and cyanobacteria. The LPS molecules from marine and terrestrial bacteria show structural variations, even among strains within the same species living in the same environment. Cyanobacterial LPS has a unique structure, since it lacks heptose and 3-deoxy-d-manno-octulosonic acid (also known as keto-deoxyoctulosonate (KDO)), which are present in the core region of common Gram-negative LPS. In addition, the cyanobacterial lipid A region lacks phosphates and contains odd-chain hydroxylated fatty acids. While the role of Gram-negative lipid A in the regulation of the innate immune response through Toll-like Receptor (TLR) 4 signaling is well characterized, the role of the structurally different cyanobacterial lipid A in TLR4 signaling is not well understood. The uncontrolled inflammatory response of TLR4 leads to autoimmune diseases such as sepsis, and thus the less virulent marine cyanobacterial LPS molecules can be effective to inhibit TLR4 signaling. This review highlights the structural comparison of LPS molecules from marine cyanobacteria and Gram-negative bacteria. We discuss the potential use of marine cyanobacterial LPS as a TLR4 antagonist, and the effects of cyanobacterial LPS on humans and marine organisms.

Proteins in the form of transcription factors (TFs) bind to specific DNA sites that regulate cell growth, differentiation, and cell development. The interactions between proteins and DNA are important toward maintaining and expressing genetic information. Without knowing TFs structures and DNA-binding properties, it is difficult to completely understand the mechanisms by which genetic information is transferred between DNA and proteins. The increasing availability of structural data on protein-DNA complexes and recognition mechanisms provides deeper insights into the nature of protein-DNA interactions and therefore, allows their manipulation. TFs utilize different mechanisms to recognize their cognate DNA (direct and indirect readouts). In this review, we focus on these recognition mechanisms as well as on the analysis of the DNA-binding domains of stem cell TFs, discussing the relative role of various amino acids toward facilitating such interactions. Unveiling such mechanisms will improve our understanding of the molecular pathways through which TFs are involved in repressing and activating gene expression.