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.

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning.

The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 is an attractive target for drug discovery due to the absence in humans and the essential role in viral replication. In this work, we developed a deep learning platform for de novo design of putative inhibitors of SARS-CoV-2 main protease (Mpro). Our methodology consists of 3 main steps: (1) training and validation of general chemistry-based generative model; (2) fine-tuning of the generative model for the chemical space of SARS-CoV- Mpro inhibitors and (3) training of a classifier for bioactivity prediction using transfer learning. The fine-tuned chemical model generated > 90% valid, diverse and novel (not present on the training set) structures. The generated molecules showed a good overlap with Mpro chemical space, displaying similar physicochemical properties and chemical structures. In addition, novel scaffolds were also generated, showing the potential to explore new chemical series. The classification model outperformed the baseline area under the precision-recall curve, showing it can be used for prediction. In addition, the model also outperformed the freely available model Chemprop on an external test set of fragments screened against SARS-CoV-2 Mpro, showing its potential to identify putative antivirals to tackle the COVID-19 pandemic. Finally, among the top-20 predicted hits, we identified nine hits via molecular docking displaying binding poses and interactions similar to experimentally validated inhibitors.

Efficient Synthesis and Antibacterial Profile of Bis(2-hydroxynaphthalene- 1,4-dione).

BACKGROUND: Antibacterial resistance is a serious public health problem infecting millions in the global population. Currently, there are few antimicrobials on the market against resistant bacterial infections. Therefore, there is an urgent need for new therapeutic options against these strains. OBJECTIVE: In this study, we synthesized and evaluated ten Bis(2-hydroxynaphthalene-1,4-dione) against Gram-positive strains, including a hospital Methicillin-resistant (MRSA), and Gram-negative strains. METHODS: The compounds were prepared by condensation of aldehydes and lawsone in the presence of different L-aminoacids as catalysts in very good yields. The compounds were submitted to antibacterial analysis through disk diffusion and Minimal Inhibitory Concentration (MIC) assays. RESULTS: L-aminoacids have been shown to be efficient catalysts in the preparation of Bis(2- hydroxynaphthalene-1,4-dione) from 2-hydroxy-1,4-naphthoquinones and arylaldehydes in excellent yields of up to 96%. The evaluation of the antibacterial profile against Gram-positive strains (Enterococcus faecalis ATCC 29212, Staphylococcus aureus ATCC 25923, S. epidermidis ATCC 12228) also including a hospital Methicillin-resistant S. aureus (MRSA) and Gram-negative strains (Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853 and Klebsiella pneumoniae ATCC 4352), revealed that seven compounds showed antibacterial activity within the Clinical and Laboratory Standards Institute (CLSI) levels mainly against P. aeruginosa ATCC 27853 (MIC 8-128 µg/mL) and MRSA (MIC 32-128 µg/mL). In addition, the in vitro toxicity showed all derivatives with no hemolytic effects on healthy human erythrocytes. Furthermore, the derivatives showed satisfactory theoretical absorption, distribution, metabolism, excretion, toxicity (ADMET) parameters, and a similar profile to antibiotics currently in use. Finally, the in silico evaluation pointed to a structure-activity relationship related to lipophilicity for these compounds. This feature may help them in acting against Gram-negative strains, which present a rich lipid cell wall selective for several antibiotics. CONCLUSION: Our data showed the potential of this series for exploring new and more effective antibacterial activities in vivo against other resistant bacteria.

Targeting CYP51 for drug design by the contributions of molecular modeling.

CYP51 is an enzyme of sterol biosynthesis pathway present in animals, plants, protozoa and fungi. This enzyme is described as an important drug target that is still of interest. Therefore, in this work, we reviewed the structure and function of CYP51 and explored the molecular modeling approaches for the development of new antifungal and antiprotozoans that target this enzyme. Crystallographic structures of CYP51 of some organisms have already been described in the literature, which enable the construction of homology models of other organisms' enzymes and molecular docking studies of new ligands. The binding mode and interactions of some new series of azoles with antifungal or antiprotozoan activities has been studied and showed important residues of the active site. Molecular modeling is an important tool to be explored for the discovery and optimization of CYP51 inhibitors with better activities, pharmacokinetics, and toxicological profiles.

Molecular modeling of a phenyl-amidine class of NMDA receptor antagonists and the rational design of new triazolyl-amidine derivatives.

Recently, many efforts have been made to develop N-methyl-D-aspartic acid receptor antagonists for treating different pathological conditions such as thrombo-embolic stroke, traumatic head injury, Huntington's, Parkinson's, and Alzheimer's diseases). However, as side-effects limit the use of most antagonists, new drugs are still required. In this work, we performed a (quantitative) structure-activity relationship analysis of 17 phenyl-amidine derivatives (1a-1q), reported as N-methyl-D-aspartic acid receptor antagonists, and used this data to rationally design the triazolyl-amidines. The best (quantitative) structure-activity relationship model constructed by multiple linear regression analysis presented high data fitting (R = 0.914) was able to explain 83.6% of the biological data variance (R(2) = 0.836), presented a satisfactory internal predictive ability (Q(2) = 0.609) and contained the descriptors (E(HOMO), Ovality and cLogP). Our assays confirmed that glutamate promotes an extensive cell death in avian neurons (77%) and 2a and 2b protected the neurons from the glutamate effect (from 77% to 27% and 45%, respectively). The results of neurotoxicity and cytotoxicity on Vero cells suggested the favorable profile of 2a and 2b. Also, the molecular modeling used to predict the activity, the interaction with the receptor and the pharmacokinetic and toxicity of the triazolyl-amidines pointed them as a promising class for further exploration as N-methyl-D-aspartic acid receptor antagonists.