Monkeypox (now Mpox), a zoonotic disease caused by the monkeypox virus (MPXV) is an emerging threat to global health. In the time span of only six months, from May to October 2022, the number of MPXV cases breached 80,000 and many of the outbreaks occurred in locations that had never previously reported MPXV. Currently there are no FDA-approved MPXV-specific vaccines or treatments, therefore, finding drugs to combat MPXV is of utmost importance. The A42R profilin-like protein of the MPXV is involved in cell development and motility making it a critical drug target. A42R protein is highly conserved across orthopoxviruses, thus A42R inhibitors may work for other family members. This study sought to identify potential A42R inhibitors for MPXV treatment using computational approaches. The energy minimized 3D structure of the A42R profilin-like protein (PDB ID: 4QWO) underwent virtual screening using a library of 36,366 compounds from Traditional Chinese Medicine (TCM), AfroDb, and PubChem databases as well as known inhibitor tecovirimat via AutoDock Vina. A total of seven compounds comprising PubChem CID: 11371962, ZINC000000899909, ZINC000001632866, ZINC000015151344, ZINC000013378519, ZINC000000086470, and ZINC000095486204, predicted to have favorable binding were shortlisted. Molecular docking suggested that all seven proposed compounds have higher binding affinities to A42R (-7.2 to -8.3 kcal/mol) than tecovirimat (-6.7 kcal/mol). This was corroborated by MM/PBSA calculations, with tecovirimat demonstrating the highest binding free energy of -68.694 kJ/mol (lowest binding affinity) compared to the seven shortlisted compounds that ranged from -73.252 to -97.140 kJ/mol. Furthermore, the 7 compounds in complex with A42R demonstrated higher stability than the A42R-tecovirimat complex when subjected to 100 ns molecular dynamics simulations. The protein-ligand interaction maps generated using LigPlot+ suggested that residues Met1, Glu3, Trp4, Ile7, Arg127, Val128, Thr131, and Asn133 are important for binding. These seven compounds were adequately profiled to be potential antivirals via PASS predictions and structural similarity searches. All seven potential lead compounds were scored Pa > Pi for antiviral activity while ZINC000001632866 and ZINC000015151344 were predicted as poxvirus inhibitors with Pa values of 0.315 and 0.215, and Pi values of 0.052 and 0.136, respectively. Further experimental validations of the identified lead compounds are required to corroborate their predicted activity. These seven identified compounds represent solid footing for development of antivirals against MPXV and other orthopoxviruses.

Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention mechanisms in the message-passing and readout phases of the MPNN, our method provides a novel architecture that integrates molecular representations at the bond, atom and molecule levels in an end-to-end way. The experimental results across nine datasets show that the proposed ABT-MPNN outperforms or is comparable to the state-of-the-art baseline models in quantitative structure-property relationship tasks. We provide case examples of Mycobacterium tuberculosis growth inhibitors and demonstrate that our model\'s visualization modality of attention at the atomic level could be an insightful way to investigate molecular atoms or functional groups associated with desired biological properties. The new model provides an innovative way to investigate the effect of self-attention on chemical substructures and functional groups in molecular representation learning, which increases the interpretability of the traditional MPNN and can serve as a valuable way to investigate the mechanism of action of drugs.

An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new graph-attention-based approach, is proposed to improve the accuracy of molecular representation by simultaneously considering atom-atom, bond-bond and atom-bond interactions. In addition, we benchmark models extensively on 8 public and 680 proprietary industrial datasets spanning a wide variety of chemical end points. The results show that ComABAN has higher prediction accuracy compared with the classical machine learning method and the deep learning-based methods. Furthermore, the trained neural network was used to predict a library of 1.5 million molecules and picked out compounds with a classification result of grade I. Subsequently, these predicted molecules were scored and ranked using cascade docking, molecular dynamics simulations to generate five potential candidates. All five molecules showed high similarity to nanomolar bioactive inhibitors suppressing the expression of HIF-1α, and we synthesized three compounds (Y-1, Y-3, Y-4) and tested their inhibitory ability in vitro. Our results indicate that ComABAN is an effective tool for accelerating drug discovery.

BACKGROUND: Infectious diseases represent a significant global strain on public health security and impact on socio-economic stability all over the world. The increasing resistance to the current antimicrobial treatment has resulted in the crucial need for the discovery and development of novel entities for the infectious treatment with different modes of action that could target both sensitive and resistant strains.
METHODS: Compounds were synthesized using the classical organic chemistry methods. Prediction of biological activity spectra was carried out using PASS and PASS-based web applications. Pharmacophore modeling in LigandScout software was used for quantitative modeling of the antibacterial activity. Antimicrobial activity was evaluated using the microdilution method. AutoDock 4.2® software was used to elucidate probable bacterial and fungal molecular targets of the studied compounds.
RESULTS: All compounds exhibited better antibacterial potency than ampicillin against all bacteria tested. Three compounds were tested against resistant strains MRSA, P. aeruginosa and E. coli and were found to be more potent than MRSA than reference drugs. All compounds demonstrated a higher degree of antifungal activity than the reference drugs bifonazole (6-17-fold) and ketoconazole (13-52-fold). Three of the most active compounds could be considered for further development of the new, more potent antimicrobial agents.
CONCLUSIONS: Compounds 5b (Z)-3-(3-hydroxyphenyl)-5-((1-methyl-1H-indol-3-yl)methylene)-2-thioxothiazolidin-4-one and 5g (Z)-3-[5-(1H-Indol-3-ylmethylene)-4-oxo-2-thioxo-thiazolidin-3-yl]-benzoic acid as well as 5h (Z)-3-(5-((5-methoxy-1H-indol-3-yl)methylene)-4-oxo-2-thioxothiazolidin-3-yl)benzoic acid can be considered as lead compounds for further development of more potent and safe antibacterial and antifungal agents.

The review is devoted to the chemical diversity of steroids produced by soft corals and their determined and potential activities. There are about 200 steroids that belong to different types of steroids such as secosteroids, spirosteroids, epoxy- and peroxy-steroids, steroid glycosides, halogenated steroids, polyoxygenated steroids and steroids containing sulfur or nitrogen heteroatoms. Of greatest interest is the pharmacological activity of these steroids. More than 40 steroids exhibit antitumor and related activity with a confidence level of over 90 percent. A group of 32 steroids shows anti-hypercholesterolemic activity with over 90 percent confidence. Ten steroids exhibit anti-inflammatory activity and 20 steroids can be classified as respiratory analeptic drugs. Several steroids exhibit rather rare and very specific activities. Steroids exhibit anti-osteoporotic properties and can be used to treat osteoporosis, as well as have strong anti-eczemic and anti-psoriatic properties and antispasmodic properties. Thus, this review is probably the first and exclusive to present the known as well as the potential pharmacological activities of 200 marine steroids.

Drug development is an arduous procedure, necessitating testing the interaction of a large number of potential candidates with potential interacting (macro)molecules. Therefore, any method which could provide an initial screening of potential candidate drugs might be of interest for the acceleration of the procedure, by highlighting interesting compounds, prior to in vitro and in vivo validation. In this line, we present a method which may identify potential hits, with agonistic and/or antagonistic properties on GPCR receptors, integrating the knowledge on signaling events triggered by receptor activation (GPCRs binding to Gα,β,γ proteins, and activating Gα , exchanging GDP for GTP, leading to a decreased affinity of the Gα for the GPCR). We show that, by integrating GPCR-ligand and Gα -GDP or -GTP binding in docking simulation, which correctly predicts crystallographic data, we can discriminate agonists, partial agonists, and antagonists, through a linear function, based on the ΔG (Gibbs-free energy) of liganded-GPCR/Gα -GDP. We built our model using two Gαs (β2-adrenergic and prostaglandin-D2 ), four Gαi (μ-opioid, dopamine-D3, adenosine-A1, rhodopsin), and one Gαo (serotonin) receptors and validated it with a series of ligands on a recently deorphanized Gαi receptor (OXER1). This approach could be a valuable tool for initial in silico validation and design of GPRC-interacting ligands.

Nowadays, the importance of computational methods in the design of therapeutic agents in a more efficient way is indisputable. Particularly, these methods have been important in the design of novel acetylcholinesterase enzyme inhibitors related to Alzheimer\'s disease. In this sense, in this report a computational model of linear prediction of acetylcholinesterase inhibitory activity of steroids and triterpenes is presented. The model is based in a correlation between binding energies obtained from molecular dynamic simulations (after docking studies) and [Formula: see text] values of a training set. This set includes a family of natural and semi-synthetic structurally related alkaloids reported in bibliography. These types of compounds, with some structural complexity, could be used as building blocks for the synthesis of many important biologically active compounds Therefore, the present study proposes an alternative based on the use of conventional and easily accessible tools to make progress on the rational design of molecules with biological activity.

Recent outbreaks of dangerous viral infections, such as Ebola virus disease, Zika fever, etc., are forcing the search for new antiviral compounds. Preferably, such compounds should possess broad-spectrum antiviral activity, as the development of drugs for the treatment of dozens of viral infections lacking specific treatment would require significant resources. Antiviral activity data present in public resources are very sparse and further investigation of structure-activity relationships is necessary. One of the strategies could be the investigation of chemical space around known active compounds and assessment of activity against closely related viruses in order to fill in the antiviral activity matrix. Here we present an investigation of antiviral activity using universal maps built with generative topographic mapping (GTM) algorithm. The GTM-based maps were used to find commercially available compounds in close proximity to already known compounds with anti-flaviviral and anti-enteroviral activities. Selected compounds were then assessed in cell-based assays against tick-borne encephalitis virus (TBEV) and a panel of enteroviruses. This approach allowed us to identify 23 new compounds showing anti-TBEV activity with EC50 values in micromolar and submicromolar range.

The Hsp70 chaperone binds and inhibits proteins implicated in apoptotic signaling including Caspase-3. Induction of apoptosis is an important mechanism of anti-cancer drugs, therefore Hsp70 can act as a protective system in tumor cells against therapeutic agents. In this study we present an assessment of candidate compounds that are able to dissociate the complex of Hsp70 with Caspase-3, and thus sensitize cells to drug-induced apoptosis. Using the PASS program for prediction of biological activity we selected a derivative of benzodioxol (BT44) that is known to affect molecular chaperones and caspases. Drug affinity responsive target stability and microscale thermophoresis assays indicated that BT44 bound to Hsp70 and reduced the chaperone activity. When etoposide was administered, heat shock accompanied with an accumulation of Hsp70 led to an inhibition of etoposide-induced apoptosis. The number of apoptotic cells increased following BT44 administration, and forced Caspase-3 processing. Competitive protein⁻protein interaction and immunoprecipitation assays showed that BT44 caused dissociation of the Hsp70⁻Caspase-3 complex, thus augmenting the anti-tumor activity of etoposide and highlighting the potential role of molecular separators in cancer therapy.

Thousands of molecules and descriptors are available for a medicinal chemist thanks to the technological advancements in different branches of chemistry. This fact as well as the correlation between them has raised new problems in quantitative structure activity relationship studies. Proper parameter initialization in statistical modeling has merged as another challenge in recent years. Random selection of parameters leads to poor performance of deep neural network (DNN). In this research, deep belief network (DBN) was applied to initialize DNNs. DBN is composed of some stacks of restricted Boltzmann machine, an energy-based method that requires computing log likelihood gradient for all samples. Three different sampling approaches were suggested to solve this gradient. In this respect, the impact of DBN was applied based on the different sampling approaches mentioned above to initialize the DNN architecture in predicting biological activity of all fifteen Kaggle targets that contain more than 70k molecules. The same as other fields of processing research, the outputs of these models demonstrated significant superiority to that of DNN with random parameters. © 2016 Wiley Periodicals, Inc.