The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN–FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.

Targeted protein degradation (TPD) techniques, particularly proteolysis-targeting chimeras (PROTAC) and molecular glue degraders (MGD), have offered novel strategies in drug discovery. With rapid advancement of computer-aided drug design (CADD) and artificial intelligence-driven drug discovery (AIDD) in the biomedical field, a major focus has become how to effectively integrate these technologies into the TPD drug discovery pipeline to accelerate development, shorten timelines, and reduce costs. Currently, the main research directions for applying CADD and AIDD in TPD include: 1) ternary complex modeling; 2) linker generation; 3) strategies to predict degrader targets, activities and ADME/T properties; 4) In silico degrader design and discovery. Models developed in these areas play a crucial role in target identification, drug design, and optimization at various stages of the discovery process. However, the limited size and quality of datasets related to TPD present challenges, leaving room for further improvement in these models. TPD involves the complex ubiquitin-proteasome system, with numerous factors influencing outcomes. Most current models adopt a static perspective to interpret and predict relevant tasks. In the future, it may be necessary to shift toward dynamic approaches that better capture the intricate relationships among these components. Furthermore, incorporating new and diverse chemical spaces will enhance the precision design and application of TPD agents.

Since the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), several variants have caused a persistent pandemic. Consequently, it is crucial to develop new potential anti-SARS-CoV-2 drugs with specificity. To minimize potential failures and preserve valuable clinical resources for the development of other useful drugs, researchers must enhance their understanding of the interactions between drugs and SARS-CoV-2. While numerous crystal structures of the SARS-CoV-2 main protease (SCM) and its inhibitors have been reported, they provide only static snapshots and fail to capture the dynamic nature of SCM/inhibitor interactions. Herein, we conducted molecular dynamics simulations for five SCM complexes: ritonavir (SCM/RTV), lopinavir (SCM/LPV), the identified inhibitor N3 (SCM/N3), the approved inhibitor ensitrelvir (SCM/ESV), and the approved drug nirmatrelvir (SCM/NMV). Additionally, we explored the potential for covalent bond formation in the N3 and NMV inhibitors through QM/MM calculations using Umbrella sampling. The results show that the binding site is highly flexible to fit those five different inhibitors and each compound has its unique binding mode at the same binding site. Moreover, the binding affinities of positive and negative inhibitors to SCM exhibit significant differences. By gaining insights into the dynamics, we can potentially elucidate why lopinavir/ritonavir, initially considered promising, failed to effectively treat COVID-19. Furthermore, understanding the mechanistic aspects of N3 and NMV inhibition on SCM not only contributes to rational drug discovery against COVID-19 but also aids future studies on the catalytic mechanisms of main proteases in other novel coronaviruses.Communicated by Ramaswamy H. Sarma