Grand Challenges for Predictive Modeling in Small Molecule Drug Discovery

Introduction Sulfur-containing functional groups are privileged motifs that occur in various pharmacologically effective substances and several natural products. Various functionalities are found with a sulfur atom at diverse oxidation states, as illustrated by thioether, sulfoxide, sulfone, sulfonamide, sulfamate, and sulfamide functions. They are valuable scaffolds in the field of medicinal chemistry and are part of a large array of approved drugs and clinical candidates. Area Covered Herein, the authors review the current research on the development of organosulfur-based drug discovery. This article also covers details of their roles in the new lead compounds reported in the literature over the past five years 2017-2021. Expert opinion Given its prominent role in medicinal chemistry and its importance in drug discovery, sulfur has attracted continuing interest and has been used in the design of various valuable compounds that demonstrate a variety of biological and pharmacological feature activities. Overall, sulfur’s role in medicinal chemistry continues to grow. However, many sulfur functionalities remain underused in small molecule drug discovery and deserve special attention in the armamentarium for treating diverse diseases. Research efforts are also still required for the development of a synthetic methodology for direct access to these functions and late-stage functionalization.

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Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity.Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5-10years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development.The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle.The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.

Innovative Partnerships for Drug Discovery against Neglected Diseases

Purpose: High-Performance Computing (HPC) is the technique of increasing the overall processing capacity of the system, and achieving high performance in solving large problems in science, engineering, or business. This research paper investigated the High-Performance Computing industry using exploratory research methodology and a listing of ABCD analysis is done. Along with this, the identification of various applications and grand challenges in HPC and various Software Tools required for industry-specific application areas of HPC is done. Design/Methodology/Approach: Qualitative exploratory research methodology to perform Industry Analysis of High-Performance Computing using ABCD Analysis. This research paper is written with the literature review of High-Performance Computing (HPC) industries and various software tools available to use in those industries. Findings/Results: Finding of this research paper is the identification of various grand challenges in HPC industries like healthcare and medical field, engineering and design, aerospace sector, urban planning, and infrastructure, finance, and business. It also lists different HPC software tools that can be used to solve these challenges, along with the findings of ABCD analysis of HPC industry. Originality/Value: Listing of ABCD Analysis (Advantages, Benefits, Constraints, and Disadvantages) of High-Performance Computing industry. Paper Type: Industry Analysis research paper

Fundamental transformations in the basic logic of computing are few and far between. Since the invention of digital computers in the early 1940s, the logic underlying computation has remained the same, even as computing hardware evolved from vacuum tubes to silicon transistors. With the advent of quantum computation, a fundamental transformation is near. Quantum computation is based on a different type of logic: rather than being in one of the two states of a classical bit, a quantum bit or qubit can be in a superposition of two states simultaneously. Operations and measurements on these qubits obey the constraints of quantum mechanics. It is now understood that quantum computers have great power in principle to go beyond classical computers, but that not every application is well suited to implementation on quantum computers. For reasons explained in more detail in the Introduction, scientific problems in chemical and materials sciences are uniquely suited to take advantage of quantum computing in the relatively near future. Indeed, quantum computing offers the best hope to solve many of the most important and difficult problems in this field. For example, quantum materials, such as superconductors and complex magnetic materials, show novel kinds of ordered phases that are natural from the point of view of quantum mechanics but difficult to access via computation on classical computers. Quantum sensors based on solid materials are already widely used but could be greatly improved with insight from quantum computations, as could materials for information technologies. Quantum chemical dynamics is another example of a problem that is intrinsically well suited to studies on quantum computers. Applications of quantum chemical dynamics include catalysis, artificial photosynthesis, and other industrially important processes. Quantum computers exist in the laboratory and are beginning to exceed 50 qubits, which is roughly the size beyond which their behavior cannot be predicted or emulated on present-day classical supercomputers. While a quantum computer of 50 qubits is almost certainly not powerful enough to tackle the major scientific challenges in chemical and materials sciences, some of these major challenges start to become accessible with a few hundred qubits if error rates can be kept small. This roundtable was convened to ask how emerging quantum computers can be applied to major scientific problems in chemical and materials sciences, in light of Basic Energy Sciences’s leading role in these fields and the Department of Energy’s leading role in high-performance scientific computation more generally. The main outcome of the roundtable was a consensus that there are scientific problems of great importance on which emerging quantum computers have the potential for disruptive impact, and where comparable progress is unlikely to occur by other means.

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.

Making Space for Anti-Infective Drug Discovery

Fluorescent RNAs: new opportunities for drug discovery.

This review provides a comprehensive analysis of the transformative impact of artificial intelligence (AI) and machine learning (ML) on modern drug design, specifically focusing on how these advanced computational techniques address the inherent limitations of traditional small-molecule drug design methodologies. It begins by outlining the historical challenges of the drug discovery pipeline, including protracted timelines, exorbitant costs, and high clinical failure rates. Subsequently, it examines the core principles of structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS), establishing the critical bottlenecks that have historically impeded efficient drug development. The central sections elucidate how cutting-edge ML and deep learning (DL) paradigms, such as generative models and reinforcement learning, are revolutionizing chemical space exploration, enhancing binding affinity prediction, improving protein flexibility modeling, and automating critical design tasks. Illustrative real-world case studies demonstrating quantifiable accelerations in discovery timelines and improved success probabilities are presented. Finally, the review critically examines prevailing challenges, including data quality, model interpretability, ethical considerations, and evolving regulatory landscapes, while offering forward-looking critical perspectives on the future trajectory of AI-driven pharmaceutical innovation.

A model approach to accelerated drug discovery

The integration of artificial intelligence (AI) with bioinformatics has initiated a transformative shift in drug discovery, redefining how pharmaceutical research and development are conducted. This review examines both the current state and emerging prospects of AI-driven strategies across the drug discovery pipeline, from target identification and molecular design to clinical applications. Advances in machine learning, deep learning, graph neural networks, transformers, foundation models, and quantum computing have shown remarkable potential in overcoming long-standing bottlenecks of conventional drug development, which on average requires 12.5 years and over $2 billion to bring a single drug to market. The AI in pharmaceuticals market, valued at $1.8 billion in 2023, is projected to reach $13.1 billion by 2030, reflecting a compound annual growth rate of 18.8%. Landmark breakthroughs such as AlphaFold, which has generated over 200 million predicted protein structures and more than 20,000 citations, have established new paradigms in structural biology and drug design, while emerging tools like RFdiffusion and AlphaFold3 further extend capabilities into de novo protein design and multi-omics integration. AI-enabled workflows have demonstrated the ability to compress discovery timelines from five years to as little as 12–18 months and reduce costs by up to 40%. Despite these advances, significant challenges remain, including issues of data quality and bias, model transparency and interpretability, computational resource demands, and ethical concerns regarding privacy and algorithmic fairness. This review synthesizes findings from recent studies to provide a roadmap for the responsible and reproducible integration of AI into bioinformatics and drug discovery, while outlining critical gaps and future research priorities essential for clinical translation.

Getting a Handle on Neuropharmacology by Targeting Receptor-Associated Proteins

High throughput screening (HTS) has become the cornerstone of lead identification for small molecules in drug discovery during the last quarter century. The evolution of the sciences and technologies that have evolved as the foundation of modern HTS campaigns are complex and require multidisciplinary interactions. Innovations in integrated automated systems, reagent systems enabled by molecular biology, computational capabilities, and visualization tools have converged to provide sophisticated tools to HTS practitioners. The success of HTS in an organization does not rest solely with those performing HTS but is critically dependent on the interactions of biology and chemistry members of the multidisciplinary teams throughout the early discovery process. Thus a basic knowledge and understanding of the components and processes of HTS is a necessary requirement for effective communication in planning, executing, and analyzing an HTS campaign. This chapter addresses the key components of HTS campaigns, common approaches, and related issues that should be understood by those engaged in small molecule drug discovery.

This project originated more than 15 years ago with the intent to produce a glossary of drug metabolism terms having definitions especially applicable for use by practicing medicinal chemists. A first-draft version underwent extensive beta-testing that, fortuitously, engaged international audiences in a wide range of disciplines involved in drug discovery and development. It became clear that the inclusion of information to enhance discussions among this mix of participants would be even more valuable. The present version retains a chemical structure theme while expanding tutorial comments that aim to bridge the various perspectives that may arise during interdisciplinary communications about a given term. This glossary is intended to be educational for early stage researchers, as well as useful for investigators at various levels who participate on today’s highly multidisciplinary, collaborative small molecule drug discovery teams.