2D and 3D similarity landscape analysis identifies PARP as a novel off-target for the drug Vatalanib

ROSHAMBO: Open-Source Molecular Alignment and 3D Similarity Scoring

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Deciphering the multi-scale mechanisms of Tephrosia purpurea against polycystic ovarian syndrome (PCOS) and its major psychiatric comorbidities: Studies from network pharmacological perspective

Tephrosia purpurea (T. purpurea), a plant belonging to Fabaceae (pea) family, is a well-known Ayurvedic herb and commonly known as Sarapunkha in traditional Indian medicinal system. Described as "Sarwa wranvishapaka", i.e. having a capability to heal all types of wounds, it is particularly recognized for its usage in splenomegaly. Towards exploring the comprehensive effects of T. purpurea against polycystic ovarian syndrome (PCOS) and three comorbid neuropsychiatric diseases (anxiety, depression, and bipolar disorder), its constituent phytochemicals (PCs) were extensively reviewed and their network pharmacology evaluation was carried out in this study. The complex regulatory potential of its 76 PCs against PCOS is enquired by developing and analyzing high confidence tripartite networks of protein targets of each phytochemical at both pathway and disease association scales. We also developed a high-confidence human Protein-Protein Interaction (PPI) sub-network specific to PCOS, explored its modular architecture, and probed 30 drug-like phytochemicals (DPCs) having multi-module regulatory potential. The phytochemicals showing good binding affinity towards their protein targets were also evaluated for similarity against currently available approved drugs present in DrugBank. Multi-targeting and synergistic capacities of 12 DPCs against 10 protein targets were identified and evaluated using molecular docking and interaction analyses. Eight DPCs as a potential source of PCOS and its comorbidity regulators are reported in T. purpurea. The results of network-pharmacology study highlight the therapeutic relevance of T. purpurea as PCOS-regulator and demonstrate the effectiveness of the approach in revealing action-mechanism of Ayurvedic herbs from holistic perspective.

SuperDRUG2: a one stop resource for approved/marketed drugs

Regular monitoring of drug regulatory agency web sites and similar resources for information on new drug approvals and changes to legal status of marketed drugs is impractical. It requires navigation through several resources to find complete information about a drug as none of the publicly accessible drug databases provide all features essential to complement in silico drug discovery. Here, we propose SuperDRUG2 (http://cheminfo.charite.de/superdrug2) as a comprehensive knowledge-base of approved and marketed drugs. We provide the largest collection of drugs (containing 4587 active pharmaceutical ingredients) which include small molecules, biological products and other drugs. The database is intended to serve as a one-stop resource providing data on: chemical structures, regulatory details, indications, drug targets, side-effects, physicochemical properties, pharmacokinetics and drug–drug interactions. We provide a 3D-superposition feature that facilitates estimation of the fit of a drug in the active site of a target with a known ligand bound to it. Apart from multiple other search options, we introduced pharmacokinetics simulation as a unique feature that allows users to visualise the ‘plasma concentration versus time’ profile for a given dose of drug with few other adjustable parameters to simulate the kinetics in a healthy individual and poor or extensive metabolisers.

Insights about multi-targeting and synergistic neuromodulators in Ayurvedic herbs against epilepsy: integrated computational studies on drug-target and protein-protein interaction networks

Epilepsy, that comprises a wide spectrum of neuronal disorders and accounts for about one percent of global disease burden affecting people of all age groups, is recognised as apasmara in the traditional medicinal system of Indian antiquity commonly known as Ayurveda. Towards exploring the molecular level complex regulatory mechanisms of 63 anti-epileptic Ayurvedic herbs and thoroughly examining the multi-targeting and synergistic potential of 349 drug-like phytochemicals (DPCs) found therein, in this study, we develop an integrated computational framework comprising of network pharmacology and molecular docking studies. Neuromodulatory prospects of anti-epileptic herbs are probed and, as a special case study, DPCs that can regulate metabotropic glutamate receptors (mGluRs) are inspected. A novel methodology to screen and systematically analyse the DPCs having similar neuromodulatory potential vis-à-vis DrugBank compounds (NeuMoDs) is developed and 11 NeuMoDs are reported. A repertoire of 74 DPCs having poly-pharmacological similarity with anti-epileptic DrugBank compounds and those under clinical trials is also reported. Further, high-confidence PPI-network specific to epileptic protein-targets is developed and the potential of DPCs to regulate its functional modules is investigated. We believe that the presented schema can open-up exhaustive explorations of indigenous herbs towards meticulous identification of clinically relevant DPCs against various diseases and disorders.

Similar compounds versus similar conformers: complementarity between PubChem 2-D and 3-D neighboring sets

BACKGROUND

PubChem is a public repository for biological activities of small molecules. For the efficient use of its vast amount of chemical information, PubChem performs 2-dimensional (2-D) and 3-dimensional (3-D) neighborings, which precompute "neighbor" relationships between molecules in the PubChem Compound database, using the PubChem subgraph fingerprints-based 2-D similarity and the Gaussian-shape overlay-based 3-D similarity, respectively. These neighborings allow PubChem to provide the user with immediate access to the list of 2-D and 3-D neighbors (also called "Similar Compounds" and "Similar Conformers", respectively) for each compound in PubChem. However, because 3-D neighboring is much more time-consuming than 2-D neighboring, how different the results of the two neighboring schemes are is an important question, considering limited computational resources.

RESULTS

The present study analyzed the complementarity between the PubChem 2-D and 3-D neighbors. When all compounds in PubChem were considered, the overlap between 2-D and 3-D neighbors was only 2% of the total neighbors. For the data sets containing compounds with annotated information, the overlap increased as the data sets became smaller. However, it did not exceed 31% and substantial fractions of neighbors were still recognized by either PubChem 2-D or 3-D similarity, but not by both. The Neighbor Preference Index (NPI) of a molecule for a given data set was introduced, which quantified whether a molecule had more 2-D or 3-D neighbors in the data set. The NPI histogram for all PubChem compounds had a bimodal shape with two maxima at NPI = ±1 and a minimum at NPI = 0. However, the NPI histograms for the subsets containing compounds with annotated information had a greater fraction of compounds with a strong preference for one neighboring method to the other (at NPI = ±1) as well as compounds with a neutral preference (at NPI = 0).

CONCLUSION

The results of our study indicate that, for the majority of the compounds in PubChem, their structural similarity to other compounds can be recognized predominantly by either 2-D or 3-D neighborings, but not by both, showing a strong complementarity between 2-D and 3-D neighboring results. Therefore, despite its heavy requirements for computational resources, 3-D neighboring provides an alternative way in which the user can instantly access structurally similar molecules that cannot be detected if only 2-D neighboring is used.Graphical AbstractThe binned distribution of the neighbor preference indices (NPIs) for all compounds in PubChem (left) has a bimodal shape with two maxima at NPI = ±1 and a minimum at NPI = 0, indicating that structural similarity between compounds in PubChem can be recognized predominantly by either 2-D or 3-D neighborings, but not by both. The NPI histogram for the drug space (right) has a greater fraction of compounds with a strong preference for one neighboring method to the other (at NPI ≈ ±1) as well as compounds with a neutral preference (at NPI ≈ 0), indicating that the drug space is very different from the PubChem space.

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

MOTIVATION

What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into today's team-based biological research.

RESULTS

ChemTreeMap is an interactive, bioinformatics tool designed to explore chemical space and mine the relationships between chemical structure, molecular properties, and biological activity. ChemTreeMap synergistically combines extended connectivity fingerprints and a neighbor-joining algorithm to produce a hierarchical tree with branch lengths proportional to molecular similarity. Compound properties are shown by leaf color, size and outline to yield a user-defined visualization of the tree. Two representative analyses are included to demonstrate ChemTreeMap's capabilities and utility: assessing dataset overlap and mining structure-activity relationships.

AVAILABILITY AND IMPLEMENTATION

The examples from this paper may be accessed at http://ajing.github.io/ChemTreeMap/ Code for the server and client are available in the Supplementary Information, at the aforementioned github site, and on Docker Hub (https://hub.docker.com) with the nametag ajing/chemtreemap.

CONTACT

carlsonh@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online.