Molecular Mechanism-Driven Discovery of Novel Small Molecule Inhibitors against Drug-Resistant SARS-CoV-2 Mpro Variants.

Under the selective pressure of nirmatrelvir, a peptidomimetic covalent drug targeting SARS-CoV-2 Mpro, various drug-resistant mutations on Mpro have been acquired in vitro. Among the mutations, L50F and E166V, along with the combination of L50F and E166V, are particularly representative and pose considerable obstacles to the effective treatment of COVID-19. Our previous study identified NMI-001 and NMI-002 as novel nonpeptide inhibitors that target SARS-CoV-2 Mpro, possessing unique scaffolds and binding modes different from those of nirmatrelvir. In view of these findings, we proposed a drug design strategy aimed at rapidly identifying inhibitors that can combat mutation-induced drug resistance. Initially, molecular dynamics (MD) simulation was employed to investigate the binding mechanisms of NMI-001 and NMI-002 against the three drug-resistant mutants (Mpro_L50F, Mpro_E166V, and Mpro_L50F+E166V). Then, we conducted two phases of high-throughput virtual screening. In the first phase, NMI-001 served as a template to perform scaffold hopping-based similarity search in a library of 15,742,661 compounds. In the second phase, 968 compounds exhibiting similarity to NMI-001 were evaluated via molecular docking and MD simulations. Six compounds that may be effective against at least one mutant were identified, and five compounds were procured for conducting in vitro assays. Finally, the compound Z1557501297 (NMI-003) exhibiting inhibitory effects against the E166V (IC50 = 27.81 ± 2.65 µM) and L50F+E166V (IC50 = 8.78 ± 0.74 µM) mutants was discovered. The binding modes referring to NMI-003-Mpro_E166V and NMI-003-Mpro_L50F+E166V were further elucidated at the atomic level. In summary, NMI-003 reported herein is the first compound with activity against E166V and L50F+E166V, which provides a good starting point to design novel antiviral drugs for the treatment of drug-resistant SARS-CoV-2.

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design.

Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Structure-Based Discovery of a Novel Allosteric Inhibitor against Human Dopamine Transporter.

Human dopamine transporter (hDAT) regulates the reuptake of extracellular dopamine (DA) and is an essential therapeutic target for central nervous system (CNS) diseases. The allosteric modulation of hDAT has been identified for decades. However, the molecular mechanism underlying the transportation is still elusive, which hinders the rational design of allosteric modulators against hDAT. Here, a systematic structure-based method was performed to explore allosteric sites on hDAT in inward-open (IO) conformation and to screen compounds with allosteric affinity. First, the model of the hDAT structure was constructed based on the recently reported Cryo-EM structure of the human serotonin transporter (hSERT) and Gaussian-accelerated molecular dynamics (GaMD) simulation was further utilized for the identification of intermediate energetic stable states of the transporter. Then, with the potential druggable allosteric site on hDAT in IO conformation, virtual screening of seven enamine chemical libraries (∼440,000 compounds) was processed, resulting in 10 compounds being purchased for in vitro assay and with Z1078601926 discovered to allosterically inhibit hDAT (IC50 = 0.527 [0.284; 0.988] µM) when nomifensine was introduced as an orthosteric ligand. Finally, the synergistic effect underlying the allosteric inhibition of hDAT by Z1078601926 and nomifensine was explored using additional GaMD simulation and postbinding free energy analysis. The hit compound discovered in this work not only provides a good starting point for lead optimization but also demonstrates the usability of the method for the structure-based discovery of novel allosteric modulators of other therapeutic targets.