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Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.

Pharmacophores are abstractions of essential chemical interaction patterns, holding an irreplaceable position in drug discovery. Despite the availability of many pharmacophore tools, the adoption of deep learning for pharmacophore-guided drug discovery remains relatively rare. We herein propose a knowledge-guided diffusion framework for ‘on-the-fly’ 3D ligand-pharmacophore mapping, named DiffPhore. It leverages ligand-pharmacophore matching knowledge to guide ligand conformation generation, meanwhile utilizing calibrated sampling to mitigate the exposure bias of the iterative conformation search process. By training on two self-established datasets of 3D ligand-pharmacophore pairs, DiffPhore achieves state-of-the-art performance in predicting ligand binding conformations, surpassing traditional pharmacophore tools and several advanced docking methods. It also manifests superior virtual screening power for lead discovery and target fishing. Using DiffPhore, we successfully identify structurally distinct inhibitors for human glutaminyl cyclases, and their binding modes are further validated through co-crystallographic analysis. We believe this work will advance the AI-enabled pharmacophore-guided drug discovery techniques. The authors develop a deep learning framework for 3D ligand pharmacophore mapping, enabling binding pose prediction, lead discovery, and target fishing. Using this approach, they identify structurally different inhibitors for human glutaminyl cyclases.

According to the latest report released by the World Health Organization, bacterial resistance to well-known and widely available antibacterial drugs has become a significant and severe global health concern and a grim challenge to tackle in order to cure infections associated with multidrug-resistant pathogenic microorganisms efficiently. Consequently, various strategies have been orchestrated to cure the severe complications related to multidrug-resistant bacteria effectively. Some approaches involved the retardation of biofilm formation and multidrug-resistance pumps in bacteria as well as the discovery of new antimicrobial agents demonstrating different mechanisms of action. In this regard, natural products namely alkaloids, terpenoids, steroids, anthraquinone, flavonoids, saponins, tannins, etc., have been suggested to tackle the multidrug-resistant bacterial strains owing to their versatile pharmacological effects. Amongst these, flavonoids, also known as polyphenolic compounds, have been widely evaluated for their antibacterial property due to their tendency to retard the growth of a wide range of pathogenic microorganisms, including multidrug-resistant bacteria. The hydroxylation of C5, C7, C3′, and C4′; and geranylation or prenylation at C6 have been extensively studied to increase bacterial inhibition of flavonoids. On the other hand, methoxylation at C3′ and C5 has been reported to decrease flavonoids’ antibacterial action. Hence, the latest information on the antibacterial activity of flavonoids is summarized in this review, with particular attention to the structure–activity relationship of this broad class of natural compounds to discover safe and potent antibacterial agents as natural products.

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.

The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process. Designing novel molecules with desired bioactivity is a critical challenge in drug discovery, particularly for novel or understudied targets. The authors propose a pharmacophore-guided deep learning approach PGMG to generate diverse active-like molecules with limited activity data.

Context As a member of a large family of proteins that together regulate various aspects of cell growth and development, the epidermal growth factor receptor (EGFR) is a validated target for the development of new drugs. Herein, we compiled a library of 62 compounds from the PubChem database with similar pharmacophores as osimertinib, which to our knowledge represents the only drug capable of overcoming EGFR-T790M-mutated NSCLC until date. Subsequently, we launched a docking-based virtual screening campaign against the EGFR kinase with the compiled chemical entities. The virtual screen identified 3 hit candidates (CID_126667097, CID_137660592, and CID_137659061) with lower binding energy/higher affinity (− 8.7 kcal/mol, − 8.6 kcal/mol, and − 8.5 kcal/mol, respectively) than the standard osimertinib (− 8.4 kcal/mol). Molecular dynamics metrics such as RMSD, RMSF, ROG, and intermolecular H-bond were used to substantiate the stability of the promising drug candidates at the binding pocket of EGFR after 100,000 ps production run. Overall, our molecular modeling study portrayed CID_126667097, CID_137660592, and CID_137659061 as lead-like drug candidates that may be further developed for the treatment of EGFR-associated cancer disease. Methods Molecular docking was conducted with Autodock Vina. A total of 62 compounds were compiled for the docking screen, which were then downloaded in SMILE format and converted to Protein Data Bank (PDB) format using the Openbabel online server. Finally, Gromacs 2022.3 was used to perform MD simulation to substantiate the stability of the hit candidates.

NAD+ plays crucial roles in various biological processe and its aberrant regulation has been suggested to be critical in the pathogenesis of diverse diseases. Intracellular NAD+ is synthesized largely from nicotinamide mononucleotide (NMN), which is the product of reaction catalyzed by nicotinamide phosphoribosyltransferase (NAMPT). Thus, the development of specific inhibitors targeting NAMPT has been suggested as a promising treatment strategy. In this study, we developed a pharmacophore-based QSAR model to discover novel NAMPT inhibitors based on diverse structural features. By virtual screening using the conformation model, we could identify eight novel active analogs having distinct pharmacophores. The biological activity of these candidates on cell viability were further examined. Our study proves the efficiency of our novel screening model and demonstrates its usefulness in the application of drug discovery process.

Achiral sulfur functional groups, such as sulfonamide, sulfone, thiol and thioether, are common in drugs and natural products. By contrast, chiral sulfur functional groups are often neglected as pharmacophores 1 , 2 – 3 , although sulfoximine, with its unique physicochemical and pharmacokinetic properties 4 , 5 , has been recently incorporated into several clinical candidates. Thus, other sulfur stereogenic centres, such as sulfinate ester, sulfinamide, sulfonimidate ester and sulfonimidamide, have started to attract attention. The diversity and complexity of these sulfur stereogenic centres have the potential to expand the chemical space for drug discovery 6 , 7 , 8 , 9 – 10 . However, the installation of these structures enantioselectively into drug molecules is highly challenging. Here we report straightforward access to enantioenriched sulfinate esters via asymmetric condensation of prochiral sulfinates and alcohols using pentanidium as an organocatalyst. We successfully coupled a wide range of sulfinates and bioactive alcohols stereoselectively. The initial sulfinates can be prepared from existing sulfone and sulfonamide drugs, and the resulting sulfinate esters are versatile for transformations to diverse chiral sulfur pharmacophores. Through late-stage diversification 11 , 12 of celecoxib and other drug derivatives, we demonstrate the viability of this unified approach towards sulfur stereogenic centres. A synthetic strategy for the stereoselective preparation of sulfinate esters and related sulfur stereogenic centres via asymmetric condensation expands the drug discovery toolbox for these compounds.

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.