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The cytosolic peptide:N‐glycanase (PNGase) is involved in the quality control of N‐glycoproteins via the endoplasmic reticulum‐associated degradation (ERAD) pathway. Mutations in the gene encoding cytosolic PNGase (NGLY1 in humans) cause NGLY1 deficiency. Recent findings indicate that the F‐box protein FBS2 of the SCFFBS2 ubiquitin ligase complex can be a promising drug target for NGLY1 deficiency. Here, we determined the crystal structure of bovine FBS2 complexed with the adaptor protein SKP1 and a sugar ligand, Man3GlcNAc2, which corresponds to the core pentasaccharide of N‐glycan. Our crystallographic data together with NMR data revealed the structural basis of disparate sugar‐binding specificities in homologous FBS proteins and identified a potential druggable pocket for in silico docking studies. Our results provide a potential basis for the development of selective inhibitors against FBS2 in NGLY1 deficiency. [ABSTRACT FROM AUTHOR]

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules’ simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands' binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors. [ABSTRACT FROM AUTHOR]

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure–activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs. [ABSTRACT FROM AUTHOR]

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (rext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC50 values in the range of 0.0508–0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB. [ABSTRACT FROM AUTHOR]

Abstract A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 105 molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 and 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~ 1.5-fold and sample-efficiency is improved ~ 45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. We find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Lastly, we show improved performance not only on recurrent neural networks but also on a reinforcement learning stabilized transformer architecture. Overall, we show that Augmented Hill-Climb improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale.

Favipiravir (brand name Avigan), a widely known anti‐influenza prodrug, is metabolized by endogenous enzymes of host cells to generate the active form, which exerts inhibition of viral RNA‐dependent RNA polymerase activity; first, favipiravir is converted to its phosphoribosylated form, favipiravir‐ribofuranosyl‐5′‐monophosphate (favipiravir‐RMP), by hypoxanthine‐guanine phosphoribosyltransferase (HGPRT). Because this phosphoribosylation reaction is the rate‐determining step in the generation of the active metabolite, quantitative and real‐time monitoring of the HGPRT‐catalyzed reaction is essential to understanding the pharmacokinetics of favipiravir. However, assay methods enabling such monitoring have not been established. 19F‐ or 31P‐based nuclear magnetic resonance (NMR) are powerful techniques for observation of intermolecular interactions, chemical reactions, and metabolism of molecules of interest, given that NMR signals of the heteronuclei sensitively reflect changes in the chemical environment of these moieties. Here, we demonstrated direct, sensitive, target‐selective, nondestructive, and real‐time observation of HGPRT‐catalyzed conversion of favipiravir to favipiravir‐RMP by performing time‐lapse 19F‐NMR monitoring of the fluorine atom of favipiravir. In addition, we showed that 31P‐NMR can be used for real‐time observation of the identical reaction by monitoring phosphorus atoms of the phosphoribosyl group of favipiravir‐RMP and of the pyrophosphate product of that reaction. Furthermore, we demonstrated that NMR approaches permit the determination of general parameters of enzymatic activity such as Vmax and Km. This method not only can be widely employed in enzyme assays, but also may be of use in the screening and development of new favipiravir‐analog antiviral prodrugs that can be phosphoribosylated more efficiently by HGPRT, which would increase the intracellular concentration of the drug's active form. The techniques demonstrated in this study would allow more detailed investigation of the pharmacokinetics of fluorinated drugs, and might significantly contribute to opening new avenues for widespread pharmaceutical studies. [ABSTRACT FROM AUTHOR]

Receptor-interacting serine/threonine kinase 2 (RIPK2) is a vital immunomodulator that plays critical roles in nucleotide-binding oligomerization domain 1 (NOD1), NOD2, and Toll-like receptors (TLRs) signaling. Stimulated NOD1 and NOD2 interact with RIPK2 and lead to the activation of nuclear factor kappa B (NF-κB) and mitogen-activated protein kinases (MAPK), followed by the production of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-12/23. Defects in NOD/RIPK2 signaling are associated with numerous inflammatory diseases, including asthma, sarcoidosis, inflammatory bowel disease (Crohn's disease and ulcerative colitis), multiple sclerosis, and Blau syndrome. As RIPK2 is a crucial element of innate immunity, small molecules regulating RIPK2 functions are attractive to establish novel immunotherapies. The increased interest in developing RIPK2 inhibitors has led to the clinical investigations of novel drug candidates. In this review, we attempt to summarize recent advances in the development of RIPK2 inhibitors and degraders. [ABSTRACT FROM AUTHOR]

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands’ binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors.

Abstract Background In 2003, the first case of severe acute respiratory syndrome coronavirus (SARS-CoV) was recorded. Coronaviruses (CoVs) have caused a major outbreak of human fatal pneumonia. Currently, there is no specific drug or treatment for diseases caused by SARS CoV 2. Computational approach that adopts dynamic models is widely accepted as indispensable tool in drug design but yet to be exploited in covid-19 in Zaria, Nigeria. In this study, steps were taken to advance on the successful achievements in the field of covid-19 drug, with the aid of in silico drug design technique, to create novel inhibitor drug candidates with better activity. In this study, one thousand human immunodeficiency virus (HIV1) antiviral chemical compounds from www.bindingBD.org were docked on the SARS CoV 2 main protease protein data bank identification number 6XBH (PDB ID: 6XBH) and the molecular docking score were ranked in order to identify the compounds with the highest inhibitory effects, and easy selection for future studies. Results The docking studies showed some interesting results. Inhibitors with Index numbers 331, 741, and 819 had the highest binding affinity. Similarly, inhibitors with Index number 441, 847, and 46 had the lowest hydrogen bond energy. Inhibitor with index number 331 was reported with the lowest value (− 48.38kCal/mol). Five new compounds were designed from the selected six (6) compounds with the best binding score giving a total of thirty (30) novel compounds. The low binding energy of inhibitor with index no. 847b is unique, as most of the interaction energies are of H-bond type with amino acids (Thr26, Gly143, Ser144, Cys145, Glu166, Gln189, Hie164, Met49, Thr26, Thr25, Thr190, Asn142, Met165) resulting in an overall negative value (−16.31 kCal/mol) making it the best of all the newly designed inhibitors. Conclusions The novel inhibitor is 2-(2-(5-amino-2-((((3-aminobenzyl)oxy)carbonyl)amino)-5-oxopentanamido)-4-(2-(tert-butyl)-4-oxo-4-(pentan-3-ylamino) butanamido)-3-hydroxybutyl) benzoic acid. The improvement it has over the parent inhibitor is from the primary amine group attached to meta position of first benzene ring and the carboxyl group attached to the ortho position of the second benzene ring. The molecular dynamics studies also show that the novel inhibitor remains stable after the study. This result makes it a better drug candidate against SARS CoV 2 main protease when compared with the co-crystallized inhibitor or any of the 1000 docked inhibitors.

Receptor-interacting serine/threonine kinase 2 (RIPK2) is a vital immunomodulator that plays critical roles in nucleotide-binding oligomerization domain 1 (NOD1), NOD2, and Toll-like receptors (TLRs) signaling. Stimulated NOD1 and NOD2 interact with RIPK2 and lead to the activation of nuclear factor kappa B (NF-κB) and mitogen-activated protein kinases (MAPK), followed by the production of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-12/23. Defects in NOD/RIPK2 signaling are associated with numerous inflammatory diseases, including asthma, sarcoidosis, inflammatory bowel disease (Crohn’s disease and ulcerative colitis), multiple sclerosis, and Blau syndrome. As RIPK2 is a crucial element of innate immunity, small molecules regulating RIPK2 functions are attractive to establish novel immunotherapies. The increased interest in developing RIPK2 inhibitors has led to the clinical investigations of novel drug candidates. In this review, we attempt to summarize recent advances in the development of RIPK2 inhibitors and degraders.

[Display omitted] • Human serum albumin has a crucial role in regulating biological system. • Understanding HSA-ligand interactions is key for DMPK optimization and efficacy. • Sub-65 kDa structures can be solved using a 200 kV microscope and routine workflows. • CryoEM may help drug discovery R&D by enabling ligand identification in HSA. Human serum albumin (HSA) is the most prevalent plasma protein in the human body, accounting for 60 % of the total plasma protein. HSA plays a major pharmacokinetic function, serving as a facilitator in the distribution of endobiotics and xenobiotics within the organism. In this paper we report the cryoEM structures of HSA in the apo form and in complex with two ligands (salicylic acid and teniposide) at a resolution of 3.5, 3.7 and 3.4 Å, respectively. We expand upon previously published work and further demonstrate that sub-4 Å maps of ∼60 kDa proteins can be routinely obtained using a 200 kV microscope, employing standard workflows. Most importantly, these maps allowed for the identification of small molecule ligands, emphasizing the practical applicability of this methodology and providing a starting point for subsequent computational modeling and in silico optimization. [ABSTRACT FROM AUTHOR]

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure–activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

KRAS‒PDEδ interaction is revealed as a promising target for suppressing the function of mutant KRAS. The bottleneck in clinical development of PDEδ inhibitors is the poor antitumor activity of known chemotypes. Here, we identified novel spiro-cyclic PDEδ inhibitors with potent antitumor activity both in vitro and in vivo. In particular, compound 36l (KD = 127 ± 16 nmol/L) effectively bound to PDEδ and interfered with KRAS–PDEδ interaction. It influenced the distribution of KRAS in Mia PaCa-2 cells, downregulated the phosphorylation of t-ERK and t-AKT and promoted apoptosis of the cells. The novel inhibitor 36l exhibited significant in vivo antitumor potency in pancreatic cancer patient-derived xenograft (PDX) models. It represents a promising lead compound for investigating the druggability of KRAS‒PDEδ interaction.

A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 105 molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 and 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~ 1.5-fold and sample-efficiency is improved ~ 45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. We find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Lastly, we show improved performance not only on recurrent neural networks but also on a reinforcement learning stabilized transformer architecture. Overall, we show that Augmented Hill-Climb improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale. [ABSTRACT FROM AUTHOR]

Motivation: This study aims to investigate non-covalent and non-peptide inhibitors of M pro , a crucial protein target, by employing a comprehensive approach that integrates molecular docking, molecular dynamics simulations, and top-hits activity predictions. The focus is on elucidating the non-covalent and non-peptide binding modes of potential inhibitors with M pro ., Methods: We employed a semi-flexible molecular docking methodology, binding score and ADME screening, which are based on structure, to screen compounds from CMNPD and HERB in silico. These methodologies allowed us to find potential candidates depending on their binding values and interactions with the binding site of main protease. To further evaluate the stability of these interactions, we conducted molecular dynamics simulations and calculated binding energies. Ultimately, a top-hits activity prediction method was employed to prioritize compounds based on their predicted inhibitory potential., Results: Through a combination of binding energy calculations and activity predictions, we identified six potential inhibitor molecules exhibiting promising activity against M pro . These compounds demonstrated favorable binding interactions and stability profiles, making them attractive candidates for further experimental validation and drug development efforts targeting M pro ., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The cytosolic peptide:N-glycanase (PNGase) is involved in the quality control of N-glycoproteins via the endoplasmic reticulum-associated degradation (ERAD) pathway. Mutations in the gene encoding cytosolic PNGase (NGLY1 in humans) cause NGLY1 deficiency. Recent findings indicate that the F-box protein FBS2 of the SCF FBS2 ubiquitin ligase complex can be a promising drug target for NGLY1 deficiency. Here, we determined the crystal structure of bovine FBS2 complexed with the adaptor protein SKP1 and a sugar ligand, Man 3 GlcNAc 2 , which corresponds to the core pentasaccharide of N-glycan. Our crystallographic data together with NMR data revealed the structural basis of disparate sugar-binding specificities in homologous FBS proteins and identified a potential druggable pocket for in silico docking studies. Our results provide a potential basis for the development of selective inhibitors against FBS2 in NGLY1 deficiency., (© 2024 Federation of European Biochemical Societies.)

Background: In 2003, the first case of severe acute respiratory syndrome coronavirus (SARS-CoV) was recorded. Coronaviruses (CoVs) have caused a major outbreak of human fatal pneumonia. Currently, there is no specific drug or treatment for diseases caused by SARS CoV 2. Computational approach that adopts dynamic models is widely accepted as indispensable tool in drug design but yet to be exploited in covid-19 in Zaria, Nigeria. In this study, steps were taken to advance on the successful achievements in the field of covid-19 drug, with the aid of in silico drug design technique, to create novel inhibitor drug candidates with better activity. In this study, one thousand human immunodeficiency virus (HIV1) antiviral chemical compounds from www.bindingBD.org were docked on the SARS CoV 2 main protease protein data bank identification number 6XBH (PDB ID: 6XBH) and the molecular docking score were ranked in order to identify the compounds with the highest inhibitory effects, and easy selection for future studies. Results: The docking studies showed some interesting results. Inhibitors with Index numbers 331, 741, and 819 had the highest binding affinity. Similarly, inhibitors with Index number 441, 847, and 46 had the lowest hydrogen bond energy. Inhibitor with index number 331 was reported with the lowest value (− 48.38kCal/mol). Five new compounds were designed from the selected six (6) compounds with the best binding score giving a total of thirty (30) novel compounds. The low binding energy of inhibitor with index no. 847b is unique, as most of the interaction energies are of H-bond type with amino acids (Thr26, Gly143, Ser144, Cys145, Glu166, Gln189, Hie164, Met49, Thr26, Thr25, Thr190, Asn142, Met165) resulting in an overall negative value (−16.31 kCal/mol) making it the best of all the newly designed inhibitors. Conclusions: The novel inhibitor is 2-(2-(5-amino-2-((((3-aminobenzyl)oxy)carbonyl)amino)-5-oxopentanamido)-4-(2-(tert-butyl)-4-oxo-4-(pentan-3-ylamino) butanamido)-3-hydroxybutyl) benzoic acid. The improvement it has over the parent inhibitor is from the primary amine group attached to meta position of first benzene ring and the carboxyl group attached to the ortho position of the second benzene ring. The molecular dynamics studies also show that the novel inhibitor remains stable after the study. This result makes it a better drug candidate against SARS CoV 2 main protease when compared with the co-crystallized inhibitor or any of the 1000 docked inhibitors. [ABSTRACT FROM AUTHOR]

Abstract Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide—a structure-based approach—as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.

Abstract Background A series of known Food and Drug Administration (FDA) approved anticancer drugs were collected from the literature and docked against mTOR receptor which has been identified in present time as a target for therapeutic anticancer agents. The compounds binding affinity were calculated after minimising the interaction within the binding pockets’ of the mTOR (4JT6) receptor. Results The result shows that PF-04691502 ligand best inhibited mTOR while occupying the Adenosine triphosphate (ATP)-binding site on the receptor. PF-04691502 had the best binding affinity with a reported value of − 39.261 kcal/mol, and a hydrogen bond energy contribution of − 8.326 kcal/mol. Polamid529 is also found to have a good binding affinity of − 36.75 kcal/mol with the receptor, but was less significant than that calculated for the reference or standard inhibitor (X6K) used (− 37.862 kcal/mol). Further analysis revealed that Palomid529 formed a more stable complex with the receptor than torin2 and X6K due to the significant hydrogen bond contributions it adds to its overall binding score. Conclusion PF-04691502 ligand was identified as the best inhibitor due to its high binding affinity for mTOR and should be considered as the best alternative to the reference inhibitor X6K.

KRAS‒PDE δ interaction is revealed as a promising target for suppressing the function of mutant KRAS. The bottleneck in clinical development of PDE δ inhibitors is the poor antitumor activity of known chemotypes. Here, we identified novel spiro-cyclic PDE δ inhibitors with potent antitumor activity both in vitro and in vivo. In particular, compound 36l (K D = 127 ± 16 nmol/L) effectively bound to PDE δ and interfered with KRAS–PDE δ interaction. It influenced the distribution of KRAS in Mia PaCa-2 cells, downregulated the phosphorylation of t-ERK and t-AKT and promoted apoptosis of the cells. The novel inhibitor 36l exhibited significant in vivo antitumor potency in pancreatic cancer patient-derived xenograft (PDX) models. It represents a promising lead compound for investigating the druggability of KRAS‒PDE δ interaction. Compound 36l is a novel spiro-cyclic PDE δ inhibitor (K D = 127 ± 16 nmol/L). It inhibited the growth of Mia PaCa-2 cells (IC 50 = 6.67 ± 1.7 μmol/L) and exhibited significant in vivo antitumor potency in pancreatic cancer patient-derived xenograft (PDX) models. [Display omitted] [ABSTRACT FROM AUTHOR]

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

The phenazine pyocyanin is an important virulence factor of the pathogen Pseudomonas aeruginosa, which is on the WHO list of antibiotic resistant "priority pathogens". In this study the isomerase PhzF, a key bacterial enzyme of the pyocyanin biosynthetic pathway, was investigated as a pathoblocker target. The aim of the pathoblocker strategy is to reduce the virulence of the pathogen without killing it, thus preventing the rapid development of resistance. Based on crystal structures of PhzF, derivatives of the inhibitor 3-hydroxyanthranilic acid were designed. Co-crystal structures of the synthesized derivatives with PhzF revealed spacial limitations of the binding pocket of PhzF in the closed conformation. In contrast, ligands aligned to the open conformation of PhzF provided more room for structural modifications. The intrinsic fluorescence of small 3-hydroxyanthranilic acid derivatives enabled direct affinity determinations using FRET assays. The analysis of structure-activity relationships showed that the carboxylic acid moiety is essential for binding to the target enzyme. The results of this study provide fundamental structural insights that will be useful for the design of PhzF-inhibitors., (© 2024 The Author(s). ChemMedChem published by Wiley-VCH GmbH.)

Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models—decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure–activity relationship model (SAR). [ABSTRACT FROM AUTHOR]

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide—a structure-based approach—as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it. [ABSTRACT FROM AUTHOR]

Background: A series of known Food and Drug Administration (FDA) approved anticancer drugs were collected from the literature and docked against mTOR receptor which has been identified in present time as a target for therapeutic anticancer agents. The compounds binding affinity were calculated after minimising the interaction within the binding pockets' of the mTOR (4JT6) receptor. Results: The result shows that PF-04691502 ligand best inhibited mTOR while occupying the Adenosine triphosphate (ATP)-binding site on the receptor. PF-04691502 had the best binding affinity with a reported value of − 39.261 kcal/mol, and a hydrogen bond energy contribution of − 8.326 kcal/mol. Polamid529 is also found to have a good binding affinity of − 36.75 kcal/mol with the receptor, but was less significant than that calculated for the reference or standard inhibitor (X6K) used (− 37.862 kcal/mol). Further analysis revealed that Palomid529 formed a more stable complex with the receptor than torin2 and X6K due to the significant hydrogen bond contributions it adds to its overall binding score. Conclusion: PF-04691502 ligand was identified as the best inhibitor due to its high binding affinity for mTOR and should be considered as the best alternative to the reference inhibitor X6K. [ABSTRACT FROM AUTHOR]

A series of known Food and Drug Administration (FDA) approved anticancer drugs were collected from the literature and docked against Glycogen synthase kinase 3β (GSKβ) receptor which has been identified in present time as a target for therapeutic anticancer agents. The compounds binding affinities (free energy of binding) were calculated after the chemical structures of the ligands were optimized and energy minimised within the binding pockets’ of the receptor GSKβ, which hasthe protein data bank identification of 3L1S presented in the work. SB 415286 was identified as the best drug for inhibiting 3L1S, its binding energy was reported as -37.288 kcal, but its significant hydrogen bond contribution given as -7.269 kcal which eclipsed all other calculated for this study, was notable. The nature of this interaction was further explored by correlating the binding affinities of the compounds with their molecular descriptors and developing realistic mathematical models using both linear and non linear regression techniques.

Recently, there has been increasing interest in new modalities such as therapeutic antibodies and gene therapy at a number of pharmaceutical companies. Moreover, in small-molecule drug discovery at such companies, efforts have focused on hard-to-drug targets such as inhibiting protein–protein interactions. Biomolecular NMR spectroscopy has been used in drug discovery in a variety of ways, such as for the reliable detection of binding and providing three-dimensional structural information for structure-based drug design. The advantages of using NMR spectroscopy have been known for decades (Jahnke in J Biomol NMR 39:87–90, (2007); Gossert and Jahnke in Prog Nucl Magn Reson Spectrosc 97:82–125, (2016)). For tackling hard-to-drug targets and increasing the success in discovering drug molecules, in-depth analysis of drug–target protein interactions performed by biophysical methods will be more and more essential. Here, we review the advantages of NMR spectroscopy as a key technology of biophysical methods and also discuss issues such as using cutting-edge NMR spectrometers and increasing the demand of utilizing conformational dynamics information for promoting small-molecule drug discovery. [ABSTRACT FROM AUTHOR]

Helicobacter pylori causes several gastric pathogeneses to human, nowadays the bacterium developed incredible drug and antibiotic resistance. The bacterium starts its activities by attachment to gastric epithelia via BabA as the main player in the\is process. The study was carried out to find/discover food constituents as inhibitors. Five molecules were obtained from the screening process, 2_3_4_5_6_Penta_O_acetyl_D_glucose, N2_N2_Dimethylguanosine, 5__Methylthioadenosine, Glyceryl_5_hydroxydecanoate, Monoisopropyl_citrate, in addition to two drugs Rivoglitazone and Tiapirinol not used for Helicobacter pylori before. The molecules were docked with considerable binding affinities with different types of interactions. The molecules were checked for the safety of different aspects, they are of good synthetic accessibility and in agreement with the Lipinski rule of 5 which is essential for Helicobacter therapy. [ABSTRACT FROM AUTHOR]

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgency. Unfortunately, this is a scenario likely to repeat itself in future, so it is of interest to explore ways in which to accelerate drug discovery at pandemic speed. Computational methods naturally lend themselves to this because they can be performed rapidly if sufficient computational resources are available. Recently, high-performance computing (HPC) technologies have led to remarkable achievements in computational drug discovery and yielded a series of new platforms, algorithms, and workflows. The application of artificial intelligence (AI) and machine learning (ML) approaches is also a promising and relatively new avenue to revolutionize the drug design process and therefore reduce costs. In this review, I describe how molecular dynamics simulations (MD) were successfully integrated with ML and adapted to HPC to form a powerful tool to study inhibitors for four of the COVID-19 target proteins. The emphasis of this review is on the strategy that was used with an explanation of each of the steps in the accelerated drug discovery workflow. For specific technical details, the reader is directed to the relevant research publications., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Natural products based on imidazole scaffolds have inspired the discovery of a wide variety of bioactive compounds. Herein, a series of imidazoles that act as competitive and potent cruzain inhibitors was investigated using a combination of ligand- and structure-based drug design strategies. Quantitative structure–activity relationships (QSARs) were generated along with the investigation of enzyme–inhibitor molecular interactions. Predictive hologram QSAR (HQSAR, r2pred = 0.80) and AutoQSAR (q2 = 0.90) models were built, and key structural properties that underpin cruzain inhibition were identified. Moreover, comparative molecular field analysis (CoMFA, r2pred = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r2pred = 0.73) revealed 3D molecular features that strongly affect the activity of the inhibitors. These findings were examined along with molecular docking studies and were highly compatible with the intermolecular contacts that take place between cruzain and the inhibitors. The results gathered herein revealed the main factors that determine the activity of the imidazoles studied and provide novel knowledge for the design of improved cruzain inhibitors.

Recent advances in calculation algorithms have led to a new level of image processing for mineral identification and mapping. Mineral outcrop mapping has a decade's history of using conventional methods like band combintion, band ratioing and relative absorption band depth (RBD) technique. Modification of these algorithms enriches the capabilities of object identification and mapping. Band combination and band ratioing help to locate the distribution of a hydrothermal altered zone. In the current study, an attempt has been made to modify the RBD approach. Newly introduced successive band depth difference (SBDD) measures the difference of reflectance values in successive bands by dividing the sum of the two highest successive shoulders by the shoulder of the lowest value before the starting shoulder. Band math function has been used in various bands of Landsat 8 operational land imager (OLI) data to access the precise distribution of points of the hydrothermal altered zone. SBDD method has achieved a kappa coefficient of 0.86 which depicts significant levels of accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] The emergence of artificial intelligence (AI) tools has transformed the landscape of drug discovery, providing unprecedented speed, efficiency, and cost-effectiveness in the search for new therapeutics. From target identification to drug formulation and delivery, AI-driven algorithms have revolutionized various aspects of medicinal chemistry, significantly accelerating the drug design process. Despite the transformative power of AI, this perspective article emphasizes the limitations of AI tools in drug discovery, requiring inventive skills of medicinal chemists. However, the article highlighted that there is a need for a harmonious integration of AI-based tools and human expertise in drug discovery. Such a synergistic approach promises to lead to groundbreaking therapies that address unmet medical needs and benefit humankind. As the world evolves technologically, the question remains: When will AI tools effectively design and develop drugs? The answer may lie in the seamless collaboration between AI and human researchers, unlocking transformative therapies that combat diseases effectively. [ABSTRACT FROM AUTHOR]

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Mycobacterium tuberculosis (Mtb) has numerous cell wall and non-cell wall mediated receptors for drug action, of which cell wall mediated targets were found to be more promising because of their pivotal role in bacterial protection and survival. Herein, we reported the design and synthesis of a series of pyrazole-linked triazoles based on the reported structural features of promising drug candidates that target DprE1 receptors through a Structure-based drug design (SBDD) approach (6a-6j and 7a-7j). The synthesized compounds were evaluated for their in-vitro antitubercular activity against virulent strains of Mtb H37Rv. In-silico studies revealed that most compounds exhibit binding interactions with crucial amino acids like Lys418, Tyr314, Tyr60, and Asp386 at DprE1. Furthermore, the protein-ligand (7j) shows appreciable stability compared to innate protein in a 100 ns molecular dynamic simulation study. In-vitro MAB assay revealed that 14 compounds exhibit significant antitubercular activity with minimum inhibitory concentration (MIC) of the 3.15-4.87 μM of the 20 compounds tested. An in-vitro cytotoxicity study on normal cell lines (MCF10) revealed safe compounds (IC 50 values:341.85 to 726.08 μM). Hence, the present study opens the development of new pyrazole-linked triazoles as probable DprE1 inhibitors., (© 2023 Wiley-VHCA AG, Zurich, Switzerland.)