Small Molecule Drug Discovery Research Articles

Open-Source Approach to GPU-Accelerated Substructure Search.

Chemical substructure search is a critical task in medicinal chemistry and small-molecule drug discovery, enabling the retrieval of molecules from databases based on specific chemical features. While systems exist for this purpose, the challenge of efficient and swift searching persists, particularly as data storage migrates to the cloud, introducing new complexities. This study provides a comprehensive analysis of chemical substructure searches, showcasing the benefits of graphics processing unit-accelerated fingerprint screening. The research highlights strategies for optimizing performance, making significant advancements in substructure searching, a pivotal aspect of drug discovery and molecular research. The accessible and scalable nature of the proposed approach makes it a valuable resource for scientists aiming to enhance their substructure search capabilities.

Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics.

Recently, remarkable progress has been achieved in artificial intelligence (AI), including machine learning. Various AI models have been proposed for drug discovery, including the design of small molecules, activity prediction, and three-dimensional (3D) structure prediction of proteins. AI consists of diverse elements, including information retrieval and machine learning, and can be used in a wide range of drug discovery scenarios. In this review, we focused on AI for small-molecule drug discovery with respect to molecular design, activity prediction, and prediction of the binding poses of compounds to target molecules. We also discussed the applications of AI in academic drug discovery.

Label-free, real-time monitoring of membrane binding events at zeptomolar concentrations using frequency-locked optical microresonators.

G-protein coupled receptors help regulate cellular function and communication, and are targets of small molecule drug discovery efforts. Conventional techniques to probe these interactions require labels and large amounts of receptor to achieve satisfactory sensitivity. Here, we use frequency-locked optical microtoroids for label-free characterization of membrane interactions in vitro at zeptomolar concentrations for the kappa opioid receptor and its native agonist dynorphin A 1-13, as well as big dynorphin (dynorphin A and dynorphin B) using a supported biomimetic membrane. The measured affinity of the agonist dynorphin A 1-13 to the κ-opioid receptor was also measured and found to be 3.1 nM. Radioligand assays revealed a dissociation constant in agreement with this value (1.1 nM). The limit of detection for the κOR/DynA 1-13 was calculated as 180 zM. The binding of Cholera Toxin B-monosialotetrahexosyl ganglioside was also monitored in real-time and an equilibrium dissociation constant of 1.53 nM was found. Our biosensing platform provides a method for highly sensitive real-time characterization of membrane embedded protein binding kinetics that is rapid and label-free, for drug discovery and toxin screening among other applications.

SMTRI: A deep learning-based web service for predicting small molecules that target miRNA-mRNA interactions

Mature microRNAs are short, single-stranded RNAs that bind to target mRNAs and induce translational repression and gene silencing. Many microRNAs discovered in animals have been implicated in diseases and have recently been pursued as therapeutic targets. However, conventional pharmacological screening for candidate small-molecule drugs can be time-consuming and labor-intensive. Therefore, developing a computational program to assist mature microRNA-targeted drug discovery in silico is desirable. Our previous work (https://doi.org/10.1002/advs.201903451) revealed that the unique functional loops formed during Argonaute-mediated microRNA-mRNA interactions have stable structural characteristics and may serve as potential targets for small molecule drug discovery. Developing drugs specifically targeting disease-related mature microRNAs and their target mRNAs would avoid affecting unrelated ones. Here, we presented SMTRI, a convolutional neural network-based approach for efficiently predicting small molecules that target RNA secondary structural motifs formed by interactions between microRNAs and their target mRNAs. Measured on three additional testing sets, SMTRI outperformed state-of-the-art algorithms by 12.9-30.3% in AUC and 2.0-18.4% in Accuracy. Moreover, four case studies on the published experimentally validated RNA-targeted small molecules also revealed the reliability of SMTRI. Our method is available as a user-friendly web server at http://smtri.net.

Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active-while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties.

Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great promise in small molecule drug discovery but have been less successful in peptide drug discovery due to limited data availability. We have developed a peptide drug discovery platform called streaMLine, enabling rigorous design, synthesis, screening, and ML-driven analysis of large peptide libraries. Using streaMLine, this study systematically explored secretin as a peptide backbone to generate potent, selective, and long-acting GLP-1R agonists with improved physicochemical properties. We synthesized and screened a total of 2688 peptides and applied ML-guided QSAR to identify multiple options for designing stable and potent GLP-1R agonists. One candidate, GUB021794, was profiled in vivo (S.C., 10 nmol/kg QD) and showed potent body weight loss in diet-induced obese mice and a half-life compatible with once-weekly dosing.

Portable fiber-based double nanohole optical tweezer for trapping small proteins

We demonstrate the trapping and analysis of individual proteins using a portable optical fiber tweezer setup with a double-nanohole in a gold film coating the fiber’s end and aligned with the fiber core. The instrument was used to trap and analyze cytochrome c, carbonic anhydrase, bovine serum albumin, and PR65 (wild-type and various point mutants). This approach was compared with a free-space optical tweezer setup that requires alignment of the laser focus to the aperture, whereas the fiber-based approach is both portable and alignment-free, which holds promise for applications in antibody discovery, small molecule drug discovery, protein interaction analysis and other applications using the standard well-plate format.

Occurrence of "Natural Selection" in Successful Small Molecule Drug Discovery.

Published compounds from ChEMBL version 32 are used to seek evidence for the occurrence of "natural selection" in drug discovery. Three measures of natural product (NP) character were applied, to compare time- and target-matched compounds reaching the clinic (clinical compounds in phase 1-3 development and approved drugs) with background compounds (reference compounds). Pseudo-NPs (PNPs), containing NP fragments combined in ways inaccessible by nature, are increasing over time, reaching 67% of clinical compounds first disclosed since 2010. PNPs are 54% more likely to be found in post-2008 clinical versus reference compounds. The majority of target classes show increased clinical compound NP character versus their reference compounds. Only 176 NP fragments appear in >1000 clinical compounds published since 2008, yet these make up on average 63% of the clinical compound's core scaffolds. There is untapped potential awaiting exploitation, by applying nature's building blocks─"natural intelligence"─to drug design.

Fluorescence Thermal Shift Assay based High Through-put Screening in Small Molecule Drug Discovery: A Brief Overview

Background: The Early drug discovery process was majorly phenotypic, lacking target specificity, and mechanism of action causing short and/or long-term toxicological impacts on the patients leading to several drug withdrawals from the market. Biochemical procedures and methods used in drug discovery and development make it lengthy in terms of time requiring approx. 15 years with the investment of billions of dollars for a new drug molecule. Technological advancement leading to the identification of protein structures through crystallography paved a new path toward targeted drug discovery. Objective: This paper provides a brief overview of the use of the fluorescence thermal shift assay (FTSA) as a high throughput (HTS) screening technique in small molecule drug discovery. Methods: The article discusses the principles of thermal denaturation techniques and how they are applied in drug discovery. It highlights the advantages of FTSA over other biophysical methods, including its label-free, cost-effective, and high HTS nature. The review also presents a comparative analysis of FTSA with techniques such as isothermal titration calorimetry (ITC), mass spectrometry (MS), nuclear magnetic resonance (NMR), surface plasmon resonance (SPR), X-ray crystallography(X-RD), circular dichroism (CD), and differential scanning calorimetry (DSC). Results: The FTSA technique is described as a powerful tool that addresses the limitations of larger protein requirements, specific assay development, and low HTS associated with other thermal denaturation methods. Unlike biochemical and cellular assays that measure secondary effects, FTSA directly estimates the binding of small molecules to target proteins. Conclusion: The review concludes that FTSA is the only biophysical method that satisfies the three key prerequisites of being label-free, cost-effective, and suitable for high HTS screening in small molecule drug discovery.

Iron(II) triflate as a photocatalyst for trifluoromethylation of functionalized arenes under blue LED light: Access to bioactive compounds

The trifluoromethyl group (-CF3) containing motifs are valuable leads for the small molecule drug discovery process and have multiple applications in medicine, agriculture, and material science. Not many CF3-containing motifs are commercially accessible; therefore, it is critical to design more advanced building blocks. In this work, we demonstrate an oxidant-free, scalable, and user-friendly protocol for the direct C(sp2)-H trifluoromethylation of functionalized arenes using Iron(II) triflate as a robust photocatalyst and bench-stable CF3SO2Na (1.5 equiv.) as a CF3 source under blue light irradiation. This method was extrapolated to >30 examples and compatible with potential functional groups (-NO2, -NH2, OH, -CHO, -CO2H, halogens, etc.). Notably, the utility of this approach is showcased for the synthesis of novel CF3-containing synthetic building blocks and a few bioactive compounds.

Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches.

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis. Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to explore hub genes associated with GBM. Bioinformatics analysis was used to screen the potential therapeutic and prognostic genes. Finally, potential small molecule drugs were predicted using the DGIdb database and verified using chemical informatics methods including absorption, distribution, metabolism, excretion, toxicity (ADMET), and molecular docking studies. Results: A total of 429 DEGs were identified, of which 19 hub genes were obtained through PPI analysis. The hub genes were confirmed as potential therapeutic targets by functional enrichment and mRNA expression. Survival analysis and protein expression confirmed centromere protein A (CENPA) as a prognostic target in GBM. Four small molecule drugs were predicted for the treatment of GBM. Conclusion: Our study suggests some promising potential therapeutic targets and small molecule drugs for the treatment of GBM, providing new ideas for further research and targeted drug development.

MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery.

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Abstract 6576: Discovery of potent and selective novel small molecule drugs to target estrogen receptor mutants in endocrine-resistant breast cancer

Abstract Introduction: The acquisition of estrogen receptor alpha (ERα) gene (ESR1) mutations is a key driver for the development of resistance to current endocrine therapy in breast cancer. Clinical studies have shown that ESR1 mutations are frequently observed in metastatic ER-positive breast cancer patients and are associated with poor survival. It is well appreciated that activating ESR1 somatic mutations, especially Y537S and D538G, can drive estrogen-independent activities. In addition, these ESR1 mutations diminish the potency of the current standard-of-care agents (tamoxifen and fulvestrant) that bind ERα directly. Therefore, it is an unmet need to develop next-generation antiestrogens that inhibit ERα mutant signaling in breast cancer to improve patient survival. Here, we search for small molecule inhibitors against ERα mutants Y537S and D538G using DNA-encoded chemical library (DEL). Methods: ERα ligand binding domain (LBD) proteins corresponding to wild type (WT), Y537S and D538G mutants were expressed in E. coli and purified by Ni-NTA, anion exchange, and size exclusion chromatography. The ability of these purified proteins to bind ligands was tested in biochemical assays to validate their use in DEL selections. We conducted a DEL affinity selection using our in-house collection of 6 billion small molecule compounds against WT, Y537S and D538G mutants ER LBD proteins in the presence and absence of estradiol. Selection hits that enriched with these targets were resynthesized off-DNA and tested in biochemical and functional studies using CRISPR-Cas9 knock-in Y537S or D538G mutant breast cancer cells. Results: We have successfully purified microgram amounts of ERα LBD for WT, Y537S, and D538G proteins. In our biochemical assay, the SRC3 peptide binds to the WT ERα LBD in the presence of estradiol, whereas Y537S and D538G LBDs bind the SRC3 peptide in the absence of estradiol, consistent with these mutants constitutively binding to SRC3. Our multibillion small molecule collections of DELs can screen and identify several hits in WT and mutant ERα LBDs. To confirm the selection output, we synthesized off-DNA compounds and validated these in biochemical and cell-based studies. Compounds CDD-1274 and CDD-1802 dramatically decrease the WT and mutant ERα protein levels in multiple ER+ breast cancer cells. These compounds greatly reduce protein levels for GREB1, TFF1, c-MYC, E2F1, and survivin in wildtype and mutant breast cancer cells, and they increase levels of cleaved PARP, a marker for apoptosis in wildtype and mutant ER+ breast cancer cells but not in ER-negative breast cancer cells. Conclusion: Our study rapidly identified potent and novel small molecule drugs to target estrogen receptor mutants using our DNA-encoded chemical library platform. Acknowledgments: Funding sources (NIH/NCI R03 CA259664 and CPRIT RP220524 to MP). Citation Format: Anil Kumar Devakrishnan, Chandrashekhar Madasu, Suzanne A.W Fuqua, Martin M. Matzuk, Murugesan Palaniappan. Discovery of potent and selective novel small molecule drugs to target estrogen receptor mutants in endocrine-resistant breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6576.

Recent Advances in Fluorescent Theranostics for Alzheimer's Disease: A Comprehensive Survey on Design, Synthesis, and Properties.

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia that is rapidly becoming a major health problem, especially in developed countries because of their increasing life expectancy. Two main problems are often associated with the disease: (i) the absence of a widely accessible "gold-standard" for early diagnosis and (ii) lack of effective therapies with disease-modifying effects. The recent success of the monoclonal antibody lecanemab played an important role not only in clarifying a possible druggable pathway but also in spelling the revival of small molecule drug discovery. Unlike bulky biologics, small molecules are structurally less complex, generally cheaper, and compatible with at-home oral consumption, making it feasible for people to start their drug regimen early and stay on it longer. In this sense, small-molecule near-infrared fluorescent theranostics have been gaining more and more attention from the scientific community, as they have the potential to simultaneously provide diagnostic outputs and deliver therapeutic action, paving the way toward personalized medicine in AD patients. They also have the potential to shift the diagnostic "status-quo" from expensive and limited-access PET radiotracers toward inexpensive and handy imaging tools widely available for primary patient screening and preclinical animal studies. Herein, we review the most recent advances in the field of fluorescent theranostics for Alzheimer's disease, detailing their design strategies, synthetic approaches and imaging and therapeutic properties in vitro and in vivo. With this Review, we intend to provide a milestone in the acquired knowledge in the field of AD theranostics, encouraging the future development of properly designed theranostic compounds with improved chances to reach clinical applications.

A virtual screening framework based on the binding site selectivity for small molecule drug discovery

Structure-based virtual screening of binding of candidate drug molecules is a topic of increasing interest in the discovery of small molecule drugs. As the same drug molecule may bind to different binding sites on a target protein, the binding site selectivity that is related to the binding tendency of candidate drug molecules to different binding sites after reaching the target protein need to be considered in sufficient details. In this work, a systematic and computer-aided virtual screening framework based on the binding site selectivity to screen candidate drug molecules in terms of their ability to bind on selected sites is presented. The framework integrates two machine learning (ML)-based models to predict the binding potential and binding selectivity to specific binding sites that are important for virtual screening of drug molecules. The details of the ML-based models together with the work-flow of the computer-aided virtual screening methods and the efficient and consistent integration of related drug design tools are presented. The applicability of this virtual screening framework is illustrated through a case study involving the screening for drug molecules as inhibitors to block the binding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to angiotensin converting enzyme 2 (ACE2), which is the target protein. The case study results point to identification of new candidate inhibitors with better binding site selectivity than two known potential inhibitors, Nilotinib and SSAA09E2.

Gut Microbiome Integration in Drug Discovery and Development of Small Molecules.

Human microbiomes, particularly in the gut, could have a major impact on the efficacy and toxicity of drugs. However, gut microbial metabolism is often neglected in the drug discovery and development process. Medicen, a Paris-based human health innovation cluster, has gathered more than 30 international leading experts from pharma, academia, biotech, clinical research organizations, and regulatory science to develop proposals to facilitate the integration of microbiome science into drug discovery and development. Seven subteams were formed to cover the complementary expertise areas of 1) pharma experience and case studies, 2) in silico microbiome-drug interaction, 3) in vitro microbial stability screening, 4) gut fermentation models, 5) animal models, 6) microbiome integration in clinical and regulatory aspects, and 7) microbiome ecosystems and models. Each expert team produced a state-of-the-art report of their respective field highlighting existing microbiome-related tools at every stage of drug discovery and development. The most critical limitations are the growing, but still limited, drug-microbiome interaction data to produce predictive models and the lack of agreed-upon standards despite recent progress. In this paper we will report on and share proposals covering 1) how microbiome tools can support moving a compound from drug discovery to clinical proof-of-concept studies and alert early on potential undesired properties stemming from microbiome-induced drug metabolism and 2) how microbiome data can be generated and integrated in pharmacokinetic models that are predictive of the human situation. Examples of drugs metabolized by the microbiome will be discussed in detail to support recommendations from the working group. SIGNIFICANCE STATEMENT: Gut microbial metabolism is often neglected in the drug discovery and development process despite growing evidence of drugs' efficacy and safety impacted by their interaction with the microbiome. This paper will detail existing microbiome-related tools covering every stage of drug discovery and development, current progress, and limitations, as well as recommendations to integrate them into the drug discovery and development process.

Optimizing high-throughput binding free energy simulations for small molecule drug discovery

Optimizing high-throughput binding free energy simulations for small molecule drug discovery

Riemannian geometry and molecular similarity I: spectrum of the Laplacian

Ligand-based virtual screening aims to reduce the cost and duration of small molecule drug discovery campaigns. Shape similarity can be used to screen large databases, with the goal of predicting potential new hits by comparing with molecules with known favourable properties. This paper presents the theory underpinning RGMolSA, a new alignment-free and mesh-free surface-based molecular shape descriptor derived from the mathematical theory of Riemannian geometry. The treatment of a molecule as a series of intersecting spheres allows the description of its surface geometry using the Riemannian metric , obtained by considering the spectrum of the Laplacian. This gives a simple vector descriptor constructed of the weighted surface area and eight non-zero eigenvalues, which capture the surface shape. We demonstrate the potential of our method by considering a series of PDE5 inhibitors that are known to have similar shape as an initial test case. RGMolSA displays promise when compared with existing shape descriptors and in its capability to handle different molecular conformers.

Patient‐derived Immunocompetent Tumor Organoids: A Platform for Chemotherapy Evaluation in the Context of T‐cell Recognition**

AbstractMost of the anticancer compounds synthesized by chemists are primarily evaluated for their direct cytotoxic effects at the cellular level, often overlooking the critical role of the immune system. In this study, we developed a patient‐derived, T‐cell‐retaining tumor organoid model that allows us to evaluate the anticancer efficacy of chemical drugs under the synergistic paradigm of antigen‐specific T‐cell‐dependent killing, which may reveal the missed drug hits in the simple cytotoxic assay. We evaluated clinically approved platinum (Pt) drugs and a custom library of twenty‐eight PtIV compounds. We observed low direct cytotoxicity of Pt drugs, but variable synergistic effects in combination with immune checkpoint inhibitors (ICIs). In contrast, the majority of PtIV compounds exhibited potent tumor‐killing capabilities. Interestingly, several PtIV compounds went beyond direct tumor killing and showed significant immunosynergistic effects with ICIs, outstanding at sub‐micromolar concentrations. Among these, Pt‐19, PtIV compounds with cinnamate axial ligands, emerged as the most therapeutically potent, demonstrating pronounced immunosynergistic effects by promoting the release of cytotoxic cytokines, activating immune‐related pathways and enhancing T cell receptor (TCR) clonal expansion. Overall, this initiative marks the first use of patient‐derived immunocompetent tumor organoids to explore and study chemotherapy, advancing their path toward more effective small molecule drug discovery.