CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures

New pyridine-based chalcones and pyrazolines with anticancer, antibacterial, and antiplasmodial activities

New pyridine-based chalcones 4a-h and pyrazolines 5a-h (N-acetyl), 6a-h (N-phenyl), and 7a-h (N-4-chlorophenyl) were synthesized and evaluated by the National Cancer Institute (NCI) against 60 different human cancer cell lines. Pyrazolines 6a, 6c-h, and 7a-h satisfied the pre-determined threshold inhibition criteria, obtaining that compounds 6c and 6f exhibited high antiproliferative activity, reaching submicromolar GI50 values from 0.38 to 0.45 μM, respectively. Moreover, compound 7g (4-CH3) exhibited the highest cytostatic activity of these series against different cancer cell lines from leukemia, nonsmall cell lung, colon, ovarian, renal, and prostate cancer, with LC50 values ranging from 5.41 to 8.35 μM, showing better cytotoxic activity than doxorubicin. Furthermore, the compounds were tested for antibacterial and antiplasmodial activities. Chalcone 4c was the most active with minimal inhibitory concentration (MIC) = 2 μg/mL against methicillin-resistant Staphylococcus aureus (MRSA), while the pyrazoline 6h showed a MIC = 8 μg/mL against Neisseria gonorrhoeae. For anti-Plasmodium falciparum activity, the chalcones display higher activity with EC50 values ranging from 10.26 to 10.94 μg/mL. Docking studies were conducted against relevant proteins from P. falciparum, exhibiting the minimum binding energy with plasmepsin II. In vivo toxicity assay in Galleria mellonella suggests that most compounds are low or nontoxic.

A potential link between aromatics-induced oviposition repellency behaviors and specific odorant receptor of Aedes albopictus

BACKGROUND

The Asian tiger mosquito, Aedes albopictus, is a competent vector for the spread of several viral arboviruses including dengue, chikungunya, and Zika. Several vital mosquito behaviors linked to survival and reproduction are primarily dependent on a sophisticated olfactory system for semiochemical perception. However, a limited number of studies has hampered our understanding of the relationship between the A. albopictus acute olfactory system and the complex chemical world.

RESULTS

Here, we performed a qRT-PCR assay on antennae from A. albopictus of differing sex, age and physiological states, and found that AalbOr10 was enriched in blood-fed female mosquitoes. We then undertook single sensillum recording to de-orphan AalbOr10 using a panel of physiologically and behaviorally relevant odorants in a Drosophila 'empty neuron' system. The results indicated that AalbOr10 was activated by seven aromatic compounds, all of which hampered egg-laying in blood-fed mosquitoes. Furthermore, using a post-RNA interference oviposition assay, we found that reducing the transcript level of AalbOr10 affected repellent activity mediated by 2-ethylphenol at low concentrations (10-4 vol/vol). Computational modeling and molecular docking studies suggested that hydrogen bonds to Y68 and Y150 mediated the interaction of 2-ethylphenol with AalbOr10.

CONCLUSION

We reveal a potential link between aromatics-induced oviposition repellency behaviors and a specific odorant receptor in A. albopictus. Our findings provide a foundation for identifying active semiochemicals for the monitoring or controlling of mosquito populations. © 2024 Society of Chemical Industry.

Four New Isocoumarins from the Mangrove Fungus Alternaria Malorum with Antimicrobial Activities

Four new isocoumarins, alternariethers A-C (1-3) and alternariester (4) were separated from the fermentation of the fungus Alternaria malorum FL39, purified from Myoporum bontioides. Their structures were ascertained using NMR and HR-ESI-MS spectroscopy. For compound 4, the absolute configuration was solved with the help of ECD calculation and the DP4+ method. Compared with the positive control triadimefon, compound 1 showed more potent antifungal effects on Colletotrichum musae. The antifungal effects of compounds 1, 2, and 3 on Fusarium oxysporum and Fusarium graminearum, of compound 4 on F. oxysporum, were equal to those of triadimefon. Except for compound 4 which was inactive against Escherichia coli with O78 serotype, all compounds showed moderate or weak antibacterial activity against Staphylococcus aureus ATCC 6538 and E. coli with O6 or O78 serotype.

Advances in machine learning with chemical language models in molecular property and reaction outcome predictions

Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets.

Identification of Potential Inhibitors of the SARS-CoV-2 NSP13 Helicase via Structure-Based Ligand Design, Molecular Docking and Nonequilibrium Alchemical Simulations

We have assembled a computational pipeline based on virtual screening, docking techniques, and nonequilibrium molecular dynamics simulations, with the goal of identifying possible inhibitors of the SARS-CoV-2 NSP13 helicase, catalyzing by ATP hydrolysis the unwinding of double or single-stranded RNA in the viral replication process inside the host cell. The druggable sites for broad-spectrum inhibitors are represented by the RNA binding sites at the 5' entrance and 3' exit of the central channel, a structural motif that is highly conserved across coronaviruses. Potential binders were first generated using structure-based ligand techniques. Their potency was estimated by using four popular docking scoring functions. Common docking hits for NSP13 were finally tested using advanced nonequilibrium alchemical techniques for binding free energy calculations on a high-performing parallel cluster. Four potential NSP13 inhibitors with potency from submicrimolar to nanomolar were finally identified.

Distance plus attention for binding affinity prediction

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution StatementThis study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model's proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

BACKGROUND

In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications.

RESULTS

To mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index.

CONCLUSION

The results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA . Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/ .

Cheminformatics approach to identify andrographolide derivatives as dual inhibitors of methyltransferases (nsp14 and nsp16) of SARS-CoV-2

The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500-700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.

Antiangiogenic potential of phytochemicals from Clerodendrum inerme (L.) Gaertn investigated through in silico and quantum computational methods

Suppressing vascular endothelial growth factor (VEGF), its receptor (VEGFR2), and the VEGF/VEGFR2 signaling cascade system to inhibit angiogenesis has emerged as a possible cancer therapeutic target. The present work was designed to discover and evaluate bioactive phytochemicals from the Clerodendrum inerme (L.) Gaertn plant for their anti-angiogenic potential. Molecular docking of twenty-one phytochemicals against the VEGFR-2 (PDB ID: 3VHE) protein was performed, followed by ADMET profiling and molecular docking simulations. These investigations unveiled two hit compounds, cirsimaritin (- 12.29 kcal/mol) and salvigenin (- 12.14 kcal/mol), with the highest binding energy values when compared to the reference drug, Sorafenib (- 15.14 kcal/mol). Furthermore, only nine phytochemicals (cirsimaritin and salvigenin included) obeyed Lipinski's rule of five and passed ADMET filters. Molecular dynamics simulations run over 100 ns revealed that the protein-ligand complexes remained stable with minimal backbone fluctuations. The binding free energy values of cirsimaritin (- 52.35 kcal/mol) and salvigenin (- 55.89 kcal/mol), deciphered by MM-GBSA analyses, further corroborated the docking interactions. The HOMO-LUMO band energy gap (ΔE) was calculated using density-functional theory (DFT) and substantiated using density of state (DOS) spectra. The chemical reactivity analyses revealed that salvigenin exhibited the highest chemical softness value (6.384 eV), the lowest hardness value (0.07831 eV), and the lowest ΔE value (0.1566 eV), which implies salvigenin was less stable and chemically more reactive than cirsimaritin and sorafenib. These findings provide further evidence that cirsimaritin and salvigenin have the ability to prevent angiogenesis and the development of cancer. Nevertheless, more in vitro and in vivo confirmation is necessary.

Further Polycyclic Quinones of Micromonospora sp

The crude acetone extract of a marine Micromonospora sp. strain associated with Eudistoma vannnamei was fractioned with hexane and ethyl acetate. The crude extract and both soluble fractions were assayed against several bacteria strains. The new polycyclic quinones 12-hydroxy-9-propyltetracene-6,1-dione (1), 5,12-dihydroxy-4-methoxy-9-propyltetracene-5,12-dione (2), and 4,6-dihydroxy-3-methoxycarbonyl- methyl-6a-(oxobutyl)-5,12-anthraquinone (3), along with the known 4,6-dihydroxy-3-methoxycarbonyl-methyl-6a-(oxo-3-methyl-butyl)-5,12-anthraquinone (4) and 4,6-dihydroxy-3-methoxycarbonyl-methyl-6a-(oxopentyl)-5,12-anthraquinone (5) were isolated from the hexane-soluble fraction, while from the active ethyl acetate fraction were isolated the known 4,6,11-trihydroxy-9-propyltetracene-5,12-dione (6), 4-methoxy-9-propyltetracene-6,11-dione (7), 7,8,9,10-tetrahydro-9-hydroxy-4-methoxy-9-propyltetracene-6,11-dione (8), and 10β-carbomethoxy-7,8,9,10-tetrahydro-4,6,7α,9α,11-pentahydroxy-9-propyltetracene-5,12-dione (9). The structures of the new compounds were established by interpretation of HRMS and NMR techniques. A study of molecular docking was performed with the compounds from the active ethyl acetate fraction to correlate tentatively with the antimicrobial activity. Molecular docking, RMSD, RMSF, and MM-GBSA evaluations were performed to investigate the inhibitory activity of 6-8 against the protein PDB-codex 1MWT, being considered a promising target for studying drug development responsible for inhibiting replication of Staphylococcus aureus. Penicillin G was used as the standard inhibitory. Anthracyclinones 6-8 were the best hydrolase inhibitor with affinity energy -8.1 to -7.9 kcal/mol compared to penicillin G, which presented -6.9 kcal/mol. Both 8 and 7 present potent inhibitory effects against hydrolase through molecular dynamics simulation and exhibit favorable drug-like properties, promising new hydrolase blockers to fight bacterial infections from Staphylococcus aureus.

Zombie cheminformatics: extraction and conversion of Wiswesser Line Notation (WLN) from chemical documents

PURPOSE

Wiswesser Line Notation (WLN) is a old line notation for encoding chemical compounds for storage and processing by computers. Whilst the notation itself has long since been surpassed by SMILES and InChI, distribution of WLN during its active years was extensive. In the context of modernising chemical data, we present a comprehensive WLN parser developed using the OpenBabel toolkit, capable of translating WLN strings into various formats supported by the library. Furthermore, we have devised a specialised Finite State Machine l, constructed from the rules of WLN, enabling the recognition and extraction of chemical strings out of large bodies of text. Available open-access WLN data with corresponding SMILES or InChI notation is rare, however ChEMBL, ChemSpider and PubChem all contain WLN records which were used for conversion scoring. Our investigation revealed a notable proportion of inaccuracies within the database entries, and we have taken steps to rectify these errors whenever feasible.

SCIENTIFIC CONTRIBUTION

Tools for both the extraction and conversion of WLN from chemical documents have been successfully developed. Both the Deterministic Finite Automaton (DFA) and parser handle the majority of WLN rules officially endorsed in the three major WLN manuals, with the parser showing a clear jump in accuracy and chemical coverage over previous submissions. The GitHub repository can be found here: https://github.com/Mblakey/wiswesser .

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Targeting C21orf58 is a Novel Treatment Strategy of Hepatocellular Carcinoma by Disrupting the Formation of JAK2/C21orf58/STAT3 Complex

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Functionally uncharacterized genes are an attractive repository to explore candidate oncogenes. It is demonstrated that C21orf58 displays an oncogenic role in promoting cell growth, tumorigenesis and sorafenib resistance of HCC cells by abnormal activation of STAT3 signaling. Mechanistically, a novel manner to regulate STAT3 signaling that adaptor C21orf58 forms a ternary complex is reveal with N-terminal domain of STAT3 and SH2 domain of JAK2, by which C21orf58 overactivates wild-type STAT3 by facilitating its phosphorylation mediated by JAK2, and hyper-activates of constitutively mutated STAT3 due to preferred binding with C21orf58 and JAK2. Moreover, it is validated that inhibition of C21orf58 with drug alminoprofen, selected by virtual screening, could effectively repress the viability and tumorigenesis of HCC cells. Therefore, it is identified that C21orf58 functions as an oncogenic adaptor, reveal a novel regulatory mechanism of JAK2/STAT3 signaling, explain the cause of abnormal activity of activated mutants of STAT3, and explore the attractive therapeutic potential by targeting C21orf58 in HCC.

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

Electricity generation from carbon dioxide adsorption by spatially nanoconfined ion separation

Selective ion transport underpins fundamental biological processes for efficient energy conversion and signal propagation. Mimicking these 'ionics' in synthetic nanofluidic channels has been increasingly promising for realizing self-sustained systems by harvesting clean energy from diverse environments, such as light, moisture, salinity gradient, etc. Here, we report a spatially nanoconfined ion separation strategy that enables harvesting electricity from CO2 adsorption. This breakthrough relies on the development of Nanosheet-Agarose Hydrogel (NAH) composite-based generators, wherein the oppositely charged ions are released in water-filled hydrogel channels upon adsorbing CO2. By tuning the ion size and ion-channel interactions, the released cations at the hundred-nanometer scale are spatially confined within the hydrogel network, while ångström-scale anions pass through unhindered. This leads to near-perfect anion/cation separation across the generator with a selectivity (D-/D+) of up to 1.8 × 106, allowing conversion into external electricity. With amplification by connecting multiple as-designed generators, the ion separation-induced electricity reaching 5 V is used to power electronic devices. This study introduces an effective spatial nanoconfinement strategy for widely demanded high-precision ion separation, encouraging a carbon-negative technique with simultaneous CO2 adsorption and energy generation.

Identification of novel potential cathepsin-B inhibitors through pharmacophore-based virtual screening, molecular docking, and dynamics simulation studies for the treatment of Alzheimer's disease

Cathepsin B is a cysteine protease lysosomal enzyme involved in several physiological functions. Overexpression of the enzyme enhances its proteolytic activity and causes the breakdown of amyloid precursor protein (APP) into neurotoxic amyloid β (Aβ), a characteristic hallmark of Alzheimer's disease (AD). Therefore, inhibition of the enzyme is a crucial therapeutic aspect for treating the disease. Combined structure and ligand-based drug design strategies were employed in the current study to identify the novel potential cathepsin B inhibitors. Five different pharmacophore models were developed and used for the screening of the ZINC-15 database. The obtained hits were analyzed for the presence of duplicates, interfering PAINS moieties, and structural similarities based on Tanimoto's coefficient. The molecular docking study was performed to screen hits with better target binding affinity. The top seven hits were selected and were further evaluated based on their predicted ADME properties. The resulting best hits, ZINC827855702, ZINC123282431, and ZINC95386847, were finally subjected to molecular dynamics simulation studies to determine the stability of the protein-ligand complex during the run. ZINC123282431 was obtained as the virtual lead compound for cathepsin B inhibition and may be a promising novel anti-Alzheimer agent.

Conformational energies of biomolecules in solution: Extending the MPCONF196 benchmark with explicit water molecules

A prerequisite for the computational prediction of molecular properties like conformational energies of biomolecules is a reliable, robust, and computationally affordable method usually selected according to its performance for relevant benchmark sets. However, most of these sets comprise molecules in the gas phase and do not cover interactions with a solvent, even though biomolecules typically occur in aqueous solution. To address this issue, we introduce a with explicit water molecules solvated version of a gas-phase benchmark set containing 196 conformers of 13 peptides and other relevant macrocycles, namely MPCONF196 [J. Řezáč et al., JCTC 2018, 14, 1254-1266], and provide very accurate PNO-LCCSD(T)-F12b/AVQZ' reference values. The novel solvMPCONF196 benchmark set features two additional challenges beyond the description of conformers in the gas phase: conformer-water and water-water interactions. The overall best performing method for this set is the double hybrid revDSDPBEP86-D4/def2-QZVPP yielding conformational energies of almost coupled cluster quality. Furthermore, some (meta-)GGAs and hybrid functionals like B97M-V and ω B97M-D with a large basis set reproduce the coupled cluster reference with an MAD below 1 kcal mol- 1  . If more efficient methods are required, the composite DFT-method r2 SCAN-3c (MAD of 1.2 kcal mol- 1 ) is a good alternative, and when conformational energies of polypeptides or macrocycles with more than 500-1000 atoms are in the focus, the semi-empirical GFN2-xTB or the MMFF94 force field (for very large systems) are recommended.

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g-1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Berberine ameliorates the progression of primary sclerosing cholangitis by activating farnesoid X receptor

Primary sclerosing cholangitis (PSC) is a rare cholestatic disease characterized by biliary infiltration, hepatic fibrosis and bile duct destruction. To date, treatment options for PSC are very limited. Therefore, the current study is aimed to investigate the therapeutic potential of berberine (BBR) against PSC. The disease was induced by feeding the mice with 3,5-diethoxycarbonyl-1,4-dihydro-2,4,6-collidine (DDC) for four weeks. The serum biochemistry and liver histology were analyzed. Furthermore, the expression of farnesoid X receptor (FXR) was also evaluated by real-time PCR. The results indicated that berberine prevents the progression of PSC by modulating the expression of FXR which ultimately regulates other genes (including Cyp7A1 and BSEP) thus maintaining bile acids homeostasis. Furthermore, the docking analysis showed that berberine interacts with the binding pocket of FXR to activate the protein thus acting as an FXR agonist. In conclusion, data indicate that berberine protects the liver from PSC-related injury. This effect might be due to the modulation of FXR activity.

Molecular docking, dynamics and in vitro analysis of multi-target inhibitors for Clostridioides difficile

The opportunistic pathogen, Clostridioides difficile owes its extreme pathogenicity for its ability to develop antibiotic resistance and recurrent infections. The current antibiotics used for the treatment are showing declining sensitivity and rising antibiotic resistance. Therefore, it is of interest to develop the anti-clostridial drugs to overcome these issues. Hence, we have explored ZINC library to find the suitable lead compounds against five target proteins of C. difficile. Multistep virtual screening is performed to find the suitable compounds that are checked for their stability using molecular dynamics and are validated in vitro against C. difficile. In our study, five compounds viz., ZINC64969876, ZINC13641164, ZINC13691348, ZINC5554596 and ZINC3894278 that inhibit HisC, Spo0A, PdcA, DAHP synthase and cyclic-di GMP proteins, respectively have been identified. Further, these compounds were tested in vitro against four different isolates of C. difficile and all of them were found to inhibit the pathogen. However, to use these compounds as anti-clostridial drugs, further testing needs to be done. The selected compounds from our study are reported for the first time as antimicrobial agents against C. difficile.

High-throughput virtual screening of organic second-order nonlinear optical chromophores within the donor-π-bridge-acceptor framework

In view of the theoretical importance and huge application potential of second-order nonlinear optical (NLO) materials, it is of great significance to conduct high-throughput virtual screening (HTVS) on a compound library to find candidate NLO chromophores. Under the donor-π-bridge-acceptor structural framework, a virtual compound library (size = 27 090) was constructed by enumeration of structural fragments. The kernel property adopted for optimization is the static first hyperpolarizability (β0). By combining machine learning and quantum chemical calculations, we have performed an HTVS procedure to sieve NLO chromophores out, and the response mechanism of the selected optimal NLO chromophores was examined. We have found: (a) The multi-layer perceptron/extended connectivity fingerprint combination with 20% selection ratio gives the highest prediction accuracy for the studied systems. (b) The two optimal donors are bis(4-diphenylaminophenyl)aminyl and bis(4-tert-butylphenyl)aminyl; the optimal π-bridges are composed of two thiophenyl, selenophenyl or furanyl units; and the two optimal acceptors are tri-s-triazinyl and 2,3-dicyanopyrazinyl. (c) The no. 1 candidate molecule can exhibit a calculated β0 equal to 8.55 × 104 a.u. (d) The difference in NLO responses of the optimal 16 molecules comes from the synergistic interaction of ES1, Δμ and f, by employing the two-level model. In addition, the sizable Δμ and f allow the studied optimal molecules to obtain a large NLO response in the meantime keeping a not-too-low excitation energy (retaining good optical transparency in the restricted range of the visible spectrum region). (e) With further modification on the acceptor, the designed DPA-π-TRZ-A' (A' = CN or NO2, π = oligo-thiophenyl or selenophenyl) systems can exhibit a rather large NLO response (maximum β0 = 3.17 × 105 a.u.), hence should have considerable potential as second-order NLO chromophores. With the above observations, we expect to provide some insight for the research community into the HTVS of organic second-order NLO chromophores.

Cobdock: an accurate and practical machine learning-based consensus blind docking method

Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

Spectroscopic Studies on Structurally Modified Anthraquinone Azo Hydrazone Tautomer: Theoretical and Experimental Approach

A series of unique four mono-azo substituted anthraquinone analogue were synthesized by using the anthraquinone components in the diazo-coupling technique. The FT-IR, 1H NMR, and HRMS, data were used to confirm the structure of the molecules, and spectroscopic techniques like UV-Vis, and photoluminescence spectroscopy were employed to estimate the photophysical properties of the molecules. The molecular optimized geometry and frontier molecular orbitals were estimated using density functional theory. Further, global chemical reactivity descriptors parameter was theoretically estimated using the value of the highest occupied molecular orbit and lowest unoccupied molecular orbits. The anti-tubercular action of the synthesised dyes were also examined. The results of this biological activity showed that N-isopropyl aniline combined with anthraquinone N-isopropyl aniline had superior anti-tubercular activity when compared to Rifampicin as the standard. As per molecular docking studies, the synthesized compound Q1 showed excellent binding energy (-10.0 kcal/mol) among all compounds against the 3ZXR Protein. These results agreed with our in-vitro anti-TB activity results.

Discovering targeted inhibitors for Escherichia coli efflux pump fusion proteins using computational and structure-guided approaches

Multidrug resistance pathogens causing infections and illness remain largely untreated clinically. Efflux pumps are one of the primary processes through which bacteria develop resistance by transferring antibiotics from the interior of their cells to the outside environment. Inhibiting these pumps by developing efficient derivatives appears to be a promising strategy for restoring antibiotic potency. This investigation explores literature-reported inhibitors of E. coli efflux pump fusion proteins AcrB-AcrA and identify potential chemical derivatives of these inhibitors to overcome the limitations. Using computational and structure-guided approaches, a study was conducted with the selected inhibitors (AcrA:25-AcrB:59) obtained by data mining and their derivatives (AcrA:857-AcrB:3891) to identify their inhibitory effect on efflux pump using virtual screening, molecular docking and density functional theory (DFT) calculations. The finding indicates that Compound 2 (ZINC000072136376) has shown better binding and a significant inhibitory effect on AcrA, while Compound 3 (ZINC000072266819) has shown stronger binding and substantial inhibition effect on both non-mutant and mutated AcrB subunits. The identified derivatives could exhibit a better inhibitor and provide a potential approach for restoring the actions of resistant antibiotics.

The metagenome-derived esterase PET40 is highly promiscuous and hydrolyses polyethylene terephthalate (PET)

Polyethylene terephthalate (PET) is a widely used synthetic polymer and known to contaminate marine and terrestrial ecosystems. Only few PET-active microorganisms and enzymes (PETases) are currently known, and it is debated whether degradation activity for PET originates from promiscuous enzymes with broad substrate spectra that primarily act on natural polymers or other bulky substrates, or whether microorganisms evolved their genetic makeup to accepting PET as a carbon source. Here, we present a predicted diene lactone hydrolase designated PET40, which acts on a broad spectrum of substrates, including PET. It is the first esterase with activity on PET from a GC-rich Gram-positive Amycolatopsis species belonging to the Pseudonocardiaceae (Actinobacteria). It is highly conserved within the genera Amycolatopsis and Streptomyces. PET40 was identified by sequence-based metagenome search using a PETase-specific hidden Markov model. Besides acting on PET, PET40 has a versatile substrate spectrum, hydrolyzing δ-lactones, β-lactam antibiotics, the polyester-polyurethane Impranil® DLN, and various para-nitrophenyl ester substrates. Molecular docking suggests that the PET degradative activity is likely a result of the promiscuity of PET40, as potential binding modes were found for substrates encompassing mono(2-hydroxyethyl) terephthalate, bis(2-hydroxyethyl) terephthalate, and a PET trimer. We also solved the crystal structure of the inactive PET40 variant S178A to 1.60 Å resolution. PET40 is active throughout a wide pH (pH 4-10) and temperature range (4-65 °C) and remarkably stable in the presence of 5% SDS, making it a promising enzyme as a starting point for further investigations and optimization approaches.

Computational prediction of analog compounds of the membrane protein MCHR1 antagonists ALB-127158 and KRX-104130

Obesity, which is already pervasive throughout the world, endangers public health by raising the prevalence of metabolic disorders and making their treatment more difficult. The development of drugs to treat obesity is a focus of effort. Melanin concentrated hormone receptor 1 (MCHR1) is the target of some of these therapeutic possibilities since as increased levels of melanin concentrated hormone have been found in obesity models. Known MCHR1 antagonists include BMS-830216, GW-856464, NGD-4715, ALB-127158, and AMG 076, but many have failed phase-I clinical studies. As a potential treatment for cardiotoxicity, KRX-104130 has only recently been identified. As MCH system is potentially effective target for treatment of obesity, in silico research into interaction between MCHR1 and its antagonists at molecular level was the primary goal of this study. Analogues ALB-127158 and KRX-104130 were screened among the RealEnamine library. The complexes obtained by molecular docking were embedded in mimics brain-cell membrane and simulated for 540 ns, and then MM-GBSA were calculated with MMPBSA.py. With all these computational studies, similar or different aspects of selected analogous compounds to ALB-127158 and KRX-104130 were investigated. The specificity of this study was that it analyzed MCHR1 protein as embedded in membrane. It was concluded that KRX-104130's analogue Z1922310273 and ALB-127158's analogue PV-002757495233 did not cause a difference in terms of phospholipid membrane properties. In addition, all ligands remained stable in putative binding site. It has been suggested that PV-002757495233 and Z1922310273 compounds can be evaluated as MCHR1 antagonists when all these outputs are considered in melting pots.

Uroprotective Potential of Campesterol in Cyclophosphamide Induced Interstitial Cystitis; Molecular Docking Studies

Cyclophosphamide (CYP) is commonly used to treat cancer of the ovaries, breast, lymph, and blood system and produces interstitial cystitis (IC) via its urotoxic metabolite: i. e., acrolein. The present study was aimed to investigate the uroprotective effect of campesterol (a steroidal phytochemical) in cyclophosphamide induced IC. IC was induced by CYP (150 mg/kg, i. p.) in rats. The Enzyme linked immunosorbent assays for oxidative stress markers and Polymerase Chain Reaction (PCR) for inflammatory cytokines were carried out. The Tissue Organ Bath Technique was used for the evaluation of the spasmolytic effect of campesterol. Different pharmacological antagonists have been used to explore the mechanism of action of campesterol. Treatment with campesterol (70 mg/kg) reduced nociception (55 %), edema (67 %), hemorrhage (67 %), and protein leakage significantly (94 %). The antioxidant activity of campesterol was exhibited by a fall in MDA, NO, and an elevation in SOD, CAT, and GPX levels. Campesterol presented anti-inflammatory potential by decreasing IL-1, TNF-α, and TGF-β expression levels. Histologically, it preserved urothelium from the deleterious effect of CYP. Campesterol showed a spasmolytic effect by reducing bladder overactivity that was dependent on muscarinic receptors, voltage-gated calcium and KATP channels, and cyclo-oxygenase pathways. In silico studies confirmed the biochemical findings. The findings suggest that campesterol could be valorized as a possible therapeutic agent against cyclophosphamide-induced interstitial cystitis.

Carbon Dots from Tire Waste for the Photodegradation of Methyl Orange Dye, Antimicrobial Activity, and Molecular Docking Study

In this study, solvothermal pathway was employed for the synthesis of P, N codoped C-dot using tire waste as a sustainable source of carbon and nitrogen. Comprehensive analyses encompassing X-ray diffraction (XRD) analysis, Transmission Electron Microscopy (TEM), FT-IR, cyclic voltammetry, and UV-Vis spectra were used to assess the crystalline structure, purity, size, fluorescence up-conversion, and morphological attributes of the nanomaterial. Subsequently, the produced C-dots were evaluated for their efficacy in the photocatalytic degradation of methylene blue and methyl orange dyes, demonstrating notable success in degrading methyl orange dye within eight hours in the visible region. Furthermore, the same nanomaterial was applied for carrying out agar disk-diffusion assays against a spectrum of microorganisms. Results revealed substantial inhibition zones against Methicillin-Resistant Staphylococcus aureus (MRSA), Escherichia coli, and Pseudomonas aeruginosa. Elucidating the antimicrobial mechanism, molecular-docking simulations were excuted using on AutoDock Vina with designated ligands. The results indicated a strong binding affinity of the C-dots with certain proteins associated with antibacterial activity. This observation suggests that the synthesized C-dots effectively engage with the active sites of these proteins, indicating their potential as promising antibacterial agents. Importantly, this study implies that C-dots do not induce protein denaturation, thereby warranting further investigation of their utility as antibacterial agents.

DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces

Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation

Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10-20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.

Extracting medicinal chemistry intuition via preference machine learning

The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist's career. In this work we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased de novo drug design. Annotated response data is provided, and developed models and code made available through a permissive open-source license.

Stereo-electronic factors influencing the stability of hydroperoxyalkyl radicals: transferability of chemical trends across hydrocarbons and ab initio methods

The hydroperoxyalkyl radicals (˙QOOH) are known to play a significant role in combustion and tropospheric processes, yet their direct spectroscopic detection remains challenging. In this study, we investigate molecular stereo-electronic effects influencing the kinetic and thermodynamic stability of a ˙QOOH along its formation path from the precursor, alkylperoxyl radical (ROO˙), and the depletion path resulting in the formation of cyclic ether + ˙OH. We focus on reactive intermediates encountered in the oxidation of acyclic hydrocarbon radicals: ethyl, isopropyl, isobutyl, tert-butyl, neopentyl, and their alicyclic counterparts: cyclohexyl, cyclohexenyl, and cyclohexadienyl. We report reaction energies and barriers calculated with the highly accurate method Weizmann-1 (W1) for the channels: ROO˙ ⇌ ˙QOOH, ROO˙ ⇌ alkene + ˙OOH, ˙QOOH ⇌ alkene + ˙OOH, and ˙QOOH ⇌ cyclic ether + ˙OH. Using W1 results as a reference, we have systematically benchmarked the accuracy of popular density functional theory (DFT), composite thermochemistry methods, and an explicitly correlated coupled-cluster method. We ascertain inductive, resonance, and steric effects on the overall stability of ˙QOOH and computationally investigate the possibility of forming more stable species. With new reactions as test cases, we probe the capacity of various ab initio methods to yield quantitative insights on the elementary steps of combustion.

G-quadruplex DNA binding properties of novel nickel Schiff base complexes with four pendant groups

Three new nickel Schiff base complexes were prepared using a two-step procedure that involves initial selective dialkylation of 2,4,6-trihydroxybenzaldehyde, followed by reaction with 1,2-phenylenediamine and nickel(II) acetate. Each of the complexes possessed the same Schiff base core but differed in the identity of the four pendant groups. All complexes were characterised by microanalysis, NMR spectroscopy and ESI mass spectrometry. In addition, two of the complexes were also characterised in the solid state using X-ray crystallography, which confirmed the presence of a square planar geometry around the metal ion. The DNA binding properties of the three nickel complexes with double stranded DNA and a range of G-quadruplex DNA structures were explored using ESI mass spectrometry, CD spectroscopy, UV melting curves, Fluorescence Resonance Energy Transfer (FRET) assays, Fluorescent Indicator Displacement (FID) assays and molecular docking studies. These techniques confirmed the ability of the three nickel complexes to bind to most of the DNA molecules examined, as well as stabilise the latter in several instances. In addition, the results of these investigations provided evidence that pendant groups with morpholine rings generally reduced DNA binding behaviour, whilst pendants featuring piperidine ring systems attached to the Schiff base core by three and not two methylene linkers often showed the greatest extent of binding or DNA stabilisation.

Investigating the hepatoprotective potentiality of marine-derived steroids as promising inhibitors of liver fibrosis

It has been reported that organic extracts derived from soft corals belonging to the genus Sarcophyton have exhibited a wide range of therapeutic characteristics. Based on biochemical and histological techniques, we aimed to assess the hepatoprotective role of the organic extract and its principal steroidal contents derived from the Red Sea soft coral Sarcophyton glaucum on acetaminophen-induced liver fibrosis in rats. Serum liver function parameters (ALT, AST, ALP and total bilirubin) were quantified using a spectrophotometer, and both alpha-fetoprotein (AFP) and carcinoembryonic antigen (CEA) levels were determined by using enzyme-linked immunosorbent assay (ELISA) kits while transformed growth factor beta (TGF-β) and tumor necrosis factor α (TNF-α) in liver tissue homogenate were determined using ELISA, and TGF-β and TNF-α gene expression in liver tissue was determined using real-time PCR following extraction and purification. Histopathological alterations in hepatic tissue were also examined under a microscope. In order to prioritize the isolation and characterization of the most promising marine steroids from the organic extract of the Red Sea soft coral Sarcophyton glaucum as hepatoprotective agents, a computational approach was employed. This approach involved molecular docking (MDock) and analysis of the structure-activity relationship (SAR) against glutathione-S-transferase (GST) and Cu-Zn human superoxide dismutase (Cu-ZnSOD) enzymes. Although the major role in the detoxification of foreign chemicals and toxic metabolites of GST and SOD enzymes is known, there is a lack of knowledge about the mode of action of the hepatoprotective process and those of the targets involved. The present study investigated the multiple interactions of a series of marine steroids with the GST and SOD enzymes, in order to reveal insights into the process of hepatoprotection.

Therapeutic Promises of Plant Metabolites against Monkeypox Virus: An In Silico Study

The monkeypox virus was still spreading in May 2022, with the first case identified in a person with travel ties to Nigeria. Using molecular docking-based techniques, we evaluated the efficiency of different bioactive chemicals obtained from plants against the monkeypox virus. A total of 56 plant compounds were evaluated for antimonekypox capabilities, with the top four candidates having a higher binding affinity than the control. We targeted the monkeypox profilin-like protein, which plays a key role in viral replication and assembly. Among the metabolites, curcumin showed the strongest binding affinity with a value of -37.43 kcal/mol, followed by gedunin (-34.89 kcal/mol), piperine (-34.58 kcal/mol), and coumadin (-34.14 kcal/mol). Based on ADME and toxicity assessments, the top four substances had no negative impacts. Furthermore, four compounds demonstrated resistance to deformability, which was corroborated by normal mode analysis. According to the bioactivity prediction study, the top compound target class was an enzyme, membrane receptor, and oxidoreductase. Furthermore, the study discovered that wortmannin, a gedunin analogue, can behave as an orthopoxvirus. The study found that these bioactive natural drug candidates could potentially work as monkeypox virus inhibitors. We recommended further experimental validation to confirm the promising findings of the study.

Retama monosperma chemical profile, green synthesis of silver nanoparticles, and antimicrobial potential: a study supported by network pharmacology and molecular docking

In this study, Retama monosperma extract (RME) was used for the green synthesis of silver nanoparticles (RME-AgNPs). RME's phenolic profile was identified by liquid chromatography coupled to mass spectroscopy (LC-ESI/MS/MS) technique. A tentative identification of 21 phenolic metabolites from the extract was performed. The produced RME-AgNPs showed UV absorbance at 443 nm. FTIR spectroscopy confirmed the presence of RME functional groups. In addition, XRD analysis confirmed the crystallography of RME-AgNPs via exhibiting peaks with 2θ values at 38.34°, 44.29°, and 64.65°. RME-AgNPs were spherical with particle sizes ranging from 9.87 to 21.16 nm, as determined by SEM and HR-TEM techniques. The zeta potential determined the particle's charge value as -15.25 mv. RME-AgNPs exhibited significantly higher antibacterial activity against Gram-negative (Escherichia coli, Pseudomonas aeruginosa, Serratia marcescens, and Klebsiella pneumoniae) and Gram-positive bacteria (Bacillus subtilis and Staphylococcus aureus) compared to RME. Moreover, the SEM images of green-synthesized nanoparticles revealed severe damage and deformation in the bacterial cell wall of the different strains subjected to the current investigation. The bioinformatics study identified 266 targets, among which only 41 targets were associated with bacterial infections. The PI3K-Akt and Relaxin signaling pathways were the top KEGG signaling pathways. Molecular docking was also performed for the 21 identified compounds at the TNF-α active site; kaempferol-3-O-robinoside-7-O-rhamnoside had a higher binding energy (-6.8084). The findings of this study warrant the use of green-synthesized AgNPs from Retama monosperma as potential antibacterial agents.

Data-driven tailoring of molecular dipole polarizability and frontier orbital energies in chemical compound space

Understanding correlations - or lack thereof - between molecular properties is crucial for enabling fast and accurate molecular design strategies. In this contribution, we explore the relation between two key quantities describing the electronic structure and chemical properties of molecular systems: the energy gap between the frontier orbitals and the dipole polarizability. Based on the recently introduced QM7-X dataset, augmented with accurate molecular polarizability calculations as well as analysis of functional group compositions, we show that polarizability and HOMO-LUMO gap are uncorrelated when considering sufficiently extended subsets of the chemical compound space. The relation between these two properties is further analyzed on specific examples of molecules with similar composition as well as homooligomers. Remarkably, the freedom brought by the lack of correlation between molecular polarizability and HOMO-LUMO gap enables the design of novel materials, as we demonstrate on the example of organic photodetector candidates.

Applied machine learning as a driver for polymeric biomaterials design

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.

An equivariant generative framework for molecular graph-structure Co-design

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% validity) and diverse (98.75% uniqueness) molecular graphs/structures with desirable properties, but also generates drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

Effect of preprocessing and simulation parameters on the performance of molecular docking studies

CONTEXT

Molecular docking is an important and rapid tool that provides a comprehensive view of different molecular mechanisms. It is often used to verify the binding interactions of many pairs of molecules and is much faster than more rigorous approaches. However, its application requires carefully preprocessing each molecule and selecting a series of simulation parameters, which is not always done correctly. We show how preprocessing and simulation parameters can positively or negatively impact molecular docking performance. For example, the inclusion of hydrogen atoms leads to better redocking scores, but molecular dynamics simulations must be performed under certain constraints; otherwise, it may worsen performance rather than improve it. This study clarifies the importance and influence of these different parameters in the simulation results.

METHODS

We analyzed the influence of different parameters on the predictive ability of molecular docking techniques using two software packages: AutoDock Vina and AutoDock-GPU. Thus, 90 receptor-ligand complexes were redocked, evaluating the root mean square deviation (RMSD) between the original position of the ligand (receptor-ligand complex obtained experimentally) and that obtained by the software for every analysis. We investigated the influence of hydrogen atoms (on the receptor and on the receptor-ligand complex), partial charges (QEq, QTPIE, EEM, EEM2015ha, MMFF94, Gasteiger-Marsili, and no charge), search boxes (size and exhaustiveness), ligand characteristics (size and number of torsions), and the use of molecular dynamics (of the receptor or the receptor-ligand complex) before docking analyses.

Nanoconfinement enabled non-covalently decorated MXene membranes for ion-sieving

Covalent modification is commonly used to tune the channel size and functionality of 2D membranes. However, common synthesis strategies used to produce such modifications are known to disrupt the structure of the membranes. Herein, we report less intrusive yet equally effective non-covalent modifications on Ti₃C₂T_x MXene membranes by a solvent treatment, where the channels are robustly decorated by protic solvents via hydrogen bond network. The densely functionalized (-O, -F, -OH) Ti₃C₂T_x channel allows multiple hydrogen bond establishment and its sub-1-nm size induces a nanoconfinement effect to greatly strengthen these interactions by maintaining solvent-MXene distance and solvent orientation. In sub-1-nm ion sieving and separation, as-decorated membranes exhibit stable ion rejection, and proton-cation (H⁺/Mⁿ⁺) selectivity that is up to 50 times and 30 times, respectively, higher than that of pristine membranes. It demonstrates the feasibility of non-covalent methods as a broad modification alternative for nanochannels integrated in energy-, resource- and environment-related applications.

Computational model for lipid binding regions in phospholipase (Ves a 1) from Vespa venom

The Thai banded tiger wasp (Vespa affinis) is a dangerous vespid species found in Southeast Asia, and its stings often result in fatalities due to the presence of lethal phospholipase A[Formula: see text], known as Vespapase or Ves a 1. Developing anti-venoms for Ves a 1 using chemical drugs, such as chemical drug guide, remains a challenging task. In this study, we screened 2056 drugs against the opening conformation of the venom using the ZINC 15 and e-Drug 3D databases. The binding free energy of the top five drug candidates complexed with Ves a 1 was calculated using 300-ns-MD trajectories. Our results revealed that voxilaprevir had a higher binding free energy at the catalytic sites than other drug candidates. Furthermore, the MD simulation results indicated that voxilaprevir formed stable conformations within the catalytic pocket. Consequently, voxilaprevir could act as a potent inhibitor, opening up avenues for the development of more effective anti-venom therapeutics for Ves a 1.

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization

Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a certain limitation that results in tokens failing to reflect the true nature of molecules. To address this issue, we developed the atom-in-SMILES tokenization scheme that eliminates ambiguities in the generic nature of SMILES tokens. Our results in multiple chemical translation and molecular property prediction tasks demonstrate that proper tokenization has a significant impact on prediction quality. In terms of prediction accuracy and token degeneration, atom-in-SMILES is more effective method in generating higher-quality SMILES sequences from AI-based chemical models compared to other tokenization and representation schemes. We investigated the degrees of token degeneration of various schemes and analyzed their adverse effects on prediction quality. Additionally, token-level repetitions were quantified, and generated examples were incorporated for qualitative examination. We believe that the atom-in-SMILES tokenization has a great potential to be adopted by broad related scientific communities, as it provides chemically accurate, tailor-made tokens for molecular property prediction, chemical translation, and molecular generative models.

Algorithm selection for protein-ligand docking: strategies and analysis on ACE

The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this problem through computational methods is beneficial in order to substantially reduce the resource and time requirements for the overall drug development process. One way of addressing protein-ligand docking is to model it as a search and optimization problem. There have been a variety of algorithmic solutions in this respect. However, there is no ultimate algorithm that can efficiently tackle this problem, both in terms of protein-ligand docking quality and speed. This argument motivates devising new algorithms, tailored to the particular protein-ligand docking scenarios. To this end, this paper reports a machine learning-based approach for improved and robust docking performance. The proposed set-up is fully automated, operating without any expert opinion or involvement both on the problem and algorithm aspects. As a case study, an empirical analysis was performed on a well-known protein, Human Angiotensin-Converting Enzyme (ACE), with 1428 ligands. For general applicability, AutoDock 4.2 was used as the docking platform. The candidate algorithms are also taken from AutoDock 4.2. Twenty-eight distinctly configured Lamarckian-Genetic Algorithm (LGA) are chosen to build an algorithm set. ALORS which is a recommender system-based algorithm selection system was preferred for automating the selection from those LGA variants on a per-instance basis. For realizing this selection automation, molecular descriptors and substructure fingerprints were employed as the features characterizing each target protein-ligand docking instance. The computational results revealed that algorithm selection outperforms all those candidate algorithms. Further assessment is reported on the algorithms space, discussing the contributions of LGA's parameters. As it pertains to protein-ligand docking, the contributions of the aforementioned features are examined, which shed light on the critical features affecting the docking performance.

Valorization of Salmo salar Skin Waste for the Synthesis of Angiotensin Converting Enzyme-1 (ACE1) Inhibitory Peptides

One of potential inhibitors which is widely used for the clinical treatment of COVID-19 in comorbid patients is Angiostensin Converting Enzyme-1 (ACE1) inhibitor. A safer peptide-based ACE1 inhibitor derived from salmon skin collagen, that is considered as the by-product of the fish processing industry have been investigated in this study. The inhibitory activity against ACE1 was examined using in vitro and in silico methods. In vitro analysis includes the extraction of acid-soluble collagen, characterization using FTIR, Raman, UV-Vis, XRD, cytotoxicity assay, and determination of inhibition against ACE1. In silico method visualizes binding affinity, molecular interaction, and inhibition type of intact collagen and active peptides derived from collagen against ACE1 using molecular docking. The results of FTIR spectra detected amide functional groups (A, B, I, II, III) and imine proline/hydroxyproline, while the results of Raman displayed peak absorption of amide I, amide III, proline/hydroxyproline ring, phenylalanine, and protein backbone. Furthermore, UV-Vis spectra showed typical collagen absorption at 230 nm and based on XRD data, the chain types in the samples were α-helix. ACE1 inhibition activity was obtained in a concentration-dependent manner where the highest was 82.83% and 85.84% at concentrations of 1000, and 2000 µg/mL, respectively, and showed very low cytotoxicity at the concentration less than 1000 µg/mL. In silico study showed an interaction between ACE1 and collagen outside the active site with the affinity of - 213.89 kcal/mol. Furthermore, the active peptides of collagen displayed greater affinity compared to lisinopril, namely HF (His-Phe), WYT (Trp-Tyr-Thr), and WF (Trp-Phe) of - 11.52; - 10.22; - 9.58 kcal/mol, respectively. The salmon skin-derived collagen demonstrated ACE1 inhibition activity with a non-competitive inhibition mechanism. In contrast, the active peptides were predicted as potent competitive inhibitors against ACE1. This study indicated that valorization of fish by-product can lead to the production of a promising bioactive compound to treat COVID-19 patient with diabetic comorbid.

Graphical Abstract

Monitoring genotoxic, biochemical and morphotoxic potential of penoxsulam and the protective role of European blueberry (Vaccinium myrtillus L.) extract

The present study aimed at exploring to explore the penoxsulam toxicity and protective effects of blueberry extract in roots of Allium cepa L. The effective concentration (EC₅₀) of penoxsulam was determined at 20 µg/L by the root growth inhibition test as the concentration reducing the root length by 50%. The bulbs of A. cepa L. were treated with tap water, blueberry extracts (25 and 50 mg/L), penoxsulam (20 µg/L) and combination of blueberry extracts (25 and 50 mg/L) with penoxsulam (20 µg/L) for 96 h. The results revealed that penoxsulam exposure inhibited cell division, rooting percentage, growth rate, root length and weight gain in the roots of A. cepa L. In addition, it induced chromosomal anomalies such as sticky chromosome, fragment, unequal distribution of chromatin, bridge, vagrant chromosome and c-mitosis and DNA strand breaks. Further, penoxsulam treatment enhanced malondialdehyde content and SOD, CAT and GR antioxidant enzyme activities. Molecular docking results supported the up-regulation of antioxidant enzyme SOD, CAT and GR. Against all these toxicity, blueberry extracts reduced penoxsulam toxicity in a concentration-dependent manner. The highest amount of recovery for cytological, morphological and oxidative stress parameters was observed when using blueberry extract at a concentration of 50 mg/L. In addition, blueberry extracts application showed a positive correlation with weight gain, root length, mitotic index and rooting percentage whereas a negative correlation with micronucleus formation, DNA damage, chromosomal aberrations, antioxidant enzymes activities and lipid peroxidation indicating its protecting effects. As a result, it has been seen that the blueberry extract can tolerate all these toxic effects of penoxsulam depending on the concentration, and it has been understood that it is a good protective natural product against such chemical exposures.

Structure and function of the metagenomic plastic-degrading polyester hydrolase PHL7 bound to its product

The recently discovered metagenomic-derived polyester hydrolase PHL7 is able to efficiently degrade amorphous polyethylene terephthalate (PET) in post-consumer plastic waste. We present the cocrystal structure of this hydrolase with its hydrolysis product terephthalic acid and elucidate the influence of 17 single mutations on the PET-hydrolytic activity and thermal stability of PHL7. The substrate-binding mode of terephthalic acid is similar to that of the thermophilic polyester hydrolase LCC and deviates from the mesophilic IsPETase. The subsite I modifications L93F and Q95Y, derived from LCC, increased the thermal stability, while exchange of H185S, derived from IsPETase, reduced the stability of PHL7. The subsite II residue H130 is suggested to represent an adaptation for high thermal stability, whereas L210 emerged as the main contributor to the observed high PET-hydrolytic activity. Variant L210T showed significantly higher activity, achieving a degradation rate of 20 µm h^-1 with amorphous PET films.

Cholesterol-Amino-Phosphate (CAP) Derived Lipid Nanoparticles for Delivery of Self-Amplifying RNA and Restoration of Spermatogenesis in Infertile Mice

Male infertility caused by genetic mutations is an important type of infertility. Currently, there is no reliable method in the clinic to address this medical need. The emergence of mRNA therapy provides a possible strategy for restoring mutant genes in the reproductive system. However, effective delivery of mRNA to spermatocytes remains a formidable challenge. Here a series of cholesterol-amino-phosphate (CAP) lipids are reported by integrating three bioactive moieties into a geometric structure, which is favorable for mRNA delivery. The results demonstrate that CAP-derived lipid nanoparticles (CAP LNPs) can deliver RNA including traditional mRNA and self-amplifying RNA (saRNA) encoding DNA Meiotic Recombinase 1 (Dmc1) protein in spermatocytes and treat male infertility caused by the Dmc1 gene mutation. Notably, the delivery efficiency of CAP LNPs is significantly higher than that of the MC3 and ALC-0315 LNPs, which is consistent with the design of CAP molecules. More importantly, a single injection of CAP LNPs-saRNA can produce Dmc1 protein for an extended period, which restores the spermatogenesis in the Dmc1 gene knockout mouse model. Overall, this study proves the concept of LNPs for the delivery of mRNA to spermatocytes, which provides a unique method to probe male infertility caused by the genetic mutation.