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Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows., (© 2024. The Author(s).)

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/)., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network (DNN) method. There are high expectations for exploration of the advanced capabilities of DNN to improve VS performance, and this capability has been optimally achieved using large data training datasets. However, their ability to screen large compound libraries has not been evaluated. There is a need for developing and evaluating ligand-based large data DNN VS models for large compound libraries. In this study, we developed ligand-based large data DNN VS models for inhibitors of six anticancer targets using 0.5 M training compounds. The developed VS models were evaluated by 10-fold cross-validation, achieving 77.9-97.8 % sensitivity, 99.9-100 % specificity, 0.82-0.98 Matthews correlation coefficient and 0.98-0.99 area under the curve, outperforming random forest models. Moreover, DNN VS models developed by pre-2015 inhibitors identified 50 % of post-2015 inhibitors with a 0.01-0.09 % false positive rate in screening 89 M PubChem compounds, also outperforming previous models. Experimental assays of the selected virtual hits of the EGFR inhibitor model led to reasonable novel structures of EGFR inhibitors. Our results confirmed the usefulness of the large data DNN model as a ligand-based VS tool to screen large compound libraries., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

PI3K/Akt/mTOR and hedgehog (Hh) signalings are two important pathways in breast cancer, which are usually connected with the drug resistance and cancer migration. Many studies indicated that PI3K/Akt/mTOR inhibitors and Hh inhibitors displayed synergistic effects, and the combination of the two signaling drugs could delay drug resistance and inhibit cancer migration in breast cancer. Therefore, the development of molecules simultaneously inhibiting these two pathways is urgent needed. Based on the structures of PI3K inhibitor buparlisib and Hh inhibitor vismodegib, a series of hybrid structures were designed and synthesized utilizing rational drug design and computer-based drug design. Several compounds displayed excellent antiproliferative activities against several breast cancer cell lines, including triple-negative breast cancer (TNBC) MDA-MB-231 cell. Further mechanistic studies demonstrated that the representative compound 9i could inhibit both PI3K/Akt/mTOR and hedgehog (Hh) signalings by inhibiting the phosphorylation of S6K and Akt as well as decreasing the SAG elevated expression of Gli1. Compound 9i could also induce apoptosis remarkably in T47D and MDA-MB-231 cells. In the transwell assay, 9i showed significant inhibition on the migration of MDA-MB-231., (Copyright © 2017 Elsevier Masson SAS. All rights reserved.)

Knowledge and investigation of therapeutic targets (responsible for drug efficacy) and the targeted drugs facilitate target and drug discovery and validation. Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/ttd/ttd.asp) has been developed to provide comprehensive information about efficacy targets and the corresponding approved, clinical trial and investigative drugs. Since its last update, major improvements and updates have been made to TTD. In addition to the significant increase of data content (from 1894 targets and 5028 drugs to 2025 targets and 17,816 drugs), we added target validation information (drug potency against target, effect against disease models and effect of target knockout, knockdown or genetic variations) for 932 targets, and 841 quantitative structure activity relationship models for active compounds of 228 chemical types against 121 targets. Moreover, we added the data from our previous drug studies including 3681 multi-target agents against 108 target pairs, 116 drug combinations with their synergistic, additive, antagonistic, potentiative or reductive mechanisms, 1427 natural product-derived approved, clinical trial and pre-clinical drugs and cross-links to the clinical trial information page in the ClinicalTrials.gov database for 770 clinical trial drugs. These updates are useful for facilitating target discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations.

Based on the literature-reported compensatory effect of PI3K on Abl inhibition and the improved preclinical effect of drug combination of Abl and PI3K inhibitors, a series of compounds bearing novel scaffold of (S)-3-aminopyrrolidine was identified as Abl and PI3K dual inhibitors through support vector machine screening tool, which were subsequently synthesized and tested. Most compounds demonstrated promising cytoxicity against a CML leukemia cell-line K562 and moderate inhibition against Abl and PI3K kinases. These compounds induced no apoptosis in K562 cell-line, suggesting that their cytotoxic activities are unlikely duo to other known anti-CML mechanisms. Molecular docking study further showed that the compound 5k could bind with both Abl and PI3K, but the weaker binding with Abl compared to Imatinib is consistent with its low kinase inhibitory rates. These plus literature-reported evidences suggest that the promising cytotoxic effect of our novel compounds might be due to the collective effect of Abl and PI3K inhibition., (Copyright © 2011 Elsevier Masson SAS. All rights reserved.)

Low target discovery rate has been linked to inadequate consideration of multiple factors that collectively contribute to druggability. These factors include sequence, structural, physicochemical, and systems profiles. Methods individually exploring each of these profiles for target identification have been developed, but they have not been collectively used. We evaluated the collective capability of these methods in identifying promising targets from 1019 research targets based on the multiple profiles of up to 348 successful targets. The collective method combining at least three profiles identified 50, 25, 10, and 4% of the 30, 84, 41, and 864 phase III, II, I, and nonclinical trial targets as promising, including eight to nine targets of positive phase III results. This method dropped 89% of the 19 discontinued clinical trial targets and 97% of the 65 targets failed in high-throughput screening or knockout studies. Collective consideration of multiple profiles demonstrated promising potential in identifying innovative targets.