Small Molecule Drug Discovery Research Articles

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 theRiemannian 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.

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

Most 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.

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.

Screening Ultra-Large Encoded Compound Libraries Leads to Novel Protein-Ligand Interactions and High Selectivity.

The DNA-encoded library (DEL) discovery platform has emerged as a powerful technology for hit identification in recent years. It has become one of the major parallel workstreams for small molecule drug discovery along with other strategies such as HTS and data mining. For many researchers working in the DEL field, it has become increasingly evident that many hits and leads discovered via DEL screening bind to target proteins with unique and unprecedented binding modes. This Perspective is our attempt to analyze reports of DEL screening with the purpose of providing a rigorous and useful account of the binding modes observed for DEL-derived ligands with a focus on binding mode novelty.

Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.

Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an F 1-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.

Validating the Use of Rational Modification of Compounds to Reduce P-gp Efflux

In both the Central nervous system (CNS) as well as Oncology small molecule drug discovery programs, efflux due to P-glycoprotein (P-gp) could be a deterrent during the discovery phase to obtain in vitro or in vivo pharmacological readouts. Several different strategies have been utilized in the past in order to overcome efflux by P-gp, many of which have been described in a recent article [5] from our labs. We describe the use of Induced-fit docking (IFD) of matched pairs (pairs of molecules modified by a single group) in order to demonstrate that a change in the IFD score, destabilizing the complex, will afford a molecule with less P-gp efflux potential. In a particular discovery program, several iterations might need to be implemented after examining the IFD lowest scored pose to obtain a molecule in that chemical series without P-gp efflux. Examples of such an effort are currently underway and will be published shortly. This summary describes therefore what we consider, a validation of a unifying approach to obtaining molecules with low to no p-gp efflux potential.

Super-resolution imaging for in situ monitoring sub-cellular micro-dynamics of small molecule drug

Small molecule drugs play a pivotal role in the arsenal of anticancer pharmacological agents. Nonetheless, their small size poses a challenge when directly visualizing their localization, distribution, mechanism of action (MOA), and target engagement at the subcellular level in real time. We propose a strategy for developing triple-functioning drug beacons that seamlessly integrate therapeutically relevant bioactivity, precise subcellular localization, and direct visualization capabilities within a single molecular entity. As a proof of concept, we have meticulously designed and constructed a boronic acid fluorescence drug beacon using coumarin–hemicyanine (CHB). Our CHB design includes three pivotal features: a boronic acid moiety that binds both adenosine triphosphate (ATP) and adenosine diphosphate (ADP), thus depleting their levels and disrupting the energy supply within mitochondria; a positively charged component that targets the drug beacon to mitochondria; and a sizeable conjugated luminophore that emits fluorescence, facilitating the application of structured illumination microscopy (SIM). Our study indicates the exceptional responsiveness of our proof-of-concept drug beacon to ADP and ATP, its efficacy in inhibiting tumor growth, and its ability to facilitate the tracking of ADP and ATP distribution around the mitochondrial cristae. Furthermore, our investigation reveals that the micro-dynamics of CHB induce mitochondrial dysfunction by causing damage to the mitochondrial cristae and mitochondrial DNA. Altogether, our findings highlight the potential of SIM in conjunction with visual drug design as a potent tool for monitoring the in situ MOA of small molecule anticancer compounds. This approach represents a crucial advancement in addressing a current challenge within the field of small molecule drug discovery and validation.

Abstract B075: Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery

Abstract Endogenous cellular metabolites have been shown to bind and regulate proteins involved in tumorigenesis. These low-affinity metabolite-protein interactions can modulate protein function through binding at allosteric sites. As an example, in normal cells, the ABL1 protein is regulated by N-terminal myristylation, which binds to a hydrophobic pocket in the C-terminal lobe of the kinase domain autoinhibiting its activity. Fusion of BCR to ABL1 leads to deletion of the N-terminal myristylation site activating BCR-ABL oncoprotein leading to chronic myeloid leukemia (CML). Similarly, autopalmitoylation of TEAD proteins regulates transcriptional output of Hippo signaling and tumorigenesis. Allosteric metabolite binding sites on these targets have been successfully used to generate new medicines (e.g.: Asciminib for BCR-ABL) and investigational cancer therapies (e.g.: IK-930, VT3989 for TEAD). However, there are limited approaches that can be broadly deployed to discover functional regulatory sites on precision oncology targets. Atavistik Bio has developed the AMPS (Atavistik Metabolite Protein Screening) platform that enables systematic and scalable screening of metabolites against proteins or protein complexes of interest. The AMPS platform technology integrates equilibrium dialysis with sensitive mass-spectrometry to screen a proprietary metabolite library. A state-of-the-art computational workflow coupled with deep mechanistic structure-function characterization enables us to discover novel functional allosteric modulators. To validate the platform, we have screened several precision oncology targets and characterized the AMPS metabolite hits with functional assays. For ABL1 tyrosine kinase, we have applied AMPS on distinct enzyme forms to probe the role of metabolite binding in the context of phosphorylation states and drug resistant T315I mutant forms. In addition, AMPS allows us to investigate novel metabolite sensitization of ABL1 in the presence of existing drugs Imatinib and Asciminib. Results from AMPS and follow up experiments highlight phosphorylation state dependency on myristate binding to ABL1. In conclusion, we demonstrate the AMPS platform can identify highly conserved allosteric protein-metabolite interactions to advance small molecule drug discovery efforts for new precision medicines. Citation Format: Anil K Padyana, Anna Schinzel, Gordon Murray, Joseph H LaPointe, Kaitlyn Selmer, Truc Pham, Melissa J Buskes, Shomit Sengupta, Maria-Jesus Blanco, Thomas Roddy, Marion Dorsch. Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B075.

Introduction to small molecule drug discovery and preclinical development

Over 90% of marketed drugs are small molecules, low molecular weight organic compounds that have been discovered, designed, and developed to prompt a specific biological process in the body. Examples include antibiotics (penicillin), analgesics (paracetamol) and synthetic hormones (corticosteroids). On average, it takes 10–15 years to develop a new medicine from initial discovery through to regulatory approval and the total cost is often in the billions. For every drug that makes it to the market, there are many more that do not, and it is the outlay associated with abortive efforts that accounts for most of this expense. The discovery of new drugs remains a significant challenge, involving teams of researchers from chemistry, biology, drug development, computer science and informatics. In this article we will discuss the key concepts and issues encountered in small molecule preclinical drug discovery and introduce some of the emerging technologies being developed to overcome current obstacles.