Surface Plasmon Resonance for Identifying and Characterising Small Molecule Ligands

It is advocated that kinetic and thermodynamic profiling of bioactive compounds should be incorporated and utilized as complementary tools for hit and lead optimizations in drug discovery. To assess their applications in the EED hit-to-lead optimization process, large amount of thermodynamic and kinetic data were collected and analyzed via isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR), respectively. Slower dissociation rates (koff) of the lead compounds were observed as the program progressed. Analysis of the kinetic data indicated that compound cellular activity correlated with both Ki and koff. Our analysis revealed that ITC data should be interpreted in the context of chiral purity of the compounds. The thermodynamic signatures of the EED aminopyrrolidine compounds were found to be mainly enthalpy driven with improved enthalpic contributions as the program progressed. Our study also demonstrated that significant challenges still exist in utilizing kinetic and thermodynamic parameters for hit selection.

Drug discovery requires the convergence of molecular attributes including magnitude and duration of exposure, tissue distribution, target engagement, and pharmacological action. To this end, during lead optimization, discovery scientists must leverage integrated data sets and translatable models to offer projections of clinical performance and thereby make informed decisions on the merits of individual molecules. This chapter presents methodologies to predict human clearance, drug-drug interaction (DDI) risk, and penetration of the blood-brain barrier (BBB) and exposure to the central nervous system during various stages of discovery with emphasis on immediate preclinical stages. By focusing on current state and best practices of the contemporary lead optimization scientist, we discuss the use of human-derived model systems and multiparameter optimization to drive the discovery of clinical candidates with favorable human ADME/PK properties in mind. We present strategies to predict and mitigate DDIs at different stages of drug discovery and development by evaluating CYP involvement in metabolism as well as achieving an assessment of a DDI’s clinical significance. We introduce concepts related to brain penetration from the perspective of small molecule drug discovery and discuss how to effectively address BBB issues in lead optimization. Emphasis is given to creation and application of preclinical data and methodologies that provide a mechanistic understanding of drug disposition leading to translatable models to predict clinical outcomes, assess developability risk, and help address simple to complex “what-if” scenarios. Predictive models of clearance, CNS penetration, and DDIs will be presented and discussed including comprehensive case studies to highlight integrated approaches used to discover drug candidates suitable for the safe exploration of clinical hypotheses.

The drug discovery process has been time-consuming and expensive in the past. Also, previous drugs were not formulated as well as the drugs being expected and developed using AI and ML technologies. This article shall elaborate on the stages of drug discovery and development where AI and ML modelling have revolutionized the traditional methods of drug development. AI, in the broader context, has been taken up and implemented at each stage of drug discovery, from target identification and validation to hit finding and the progression from hit to lead optimization, with a vital role in streamlining the formerly time-consuming process of drug screening. Several methods based on machine learning are being used to predict drug targets, predict the structures of drug targets, predict binding sites, perform ligand-based similarity searches, design ligands with certain desired properties de novo, develop scoring functions for molecular docking, build Quantitative structure-activity relationship (QSAR) models for the prediction of biological activity, and predict the pharmacokinetic and pharmacodynamic properties of ligands.

Drug discovery is a multifaceted and resource-intensive process traditionally marked by long development timelines, high financial costs, and significant attrition rates. The integration of Artificial Intelligence (AI) has introduced transformative opportunities to overcome these barriers by enhancing data-driven decision-making, accelerating molecular design, and improving clinical translation. This chapter explores the foundations, applications, and case studies of AI across all stages of drug discovery - from target identification and validation to lead optimization and clinical development. Real-world examples, including BenevolentAI, Atomwise, Insilico Medicine, and the Pfizer–IBM Watson collaboration, illustrate AI’s role in accelerating drug design, repurposing, and optimization. Despite its promise, challenges such as data quality, model interpretability, regulatory uncertainty, and intellectual property disputes continue to impede widespread adoption. The chapter concludes by emphasizing the future directions of AI in synergy with quantum computing, multi-omics integration, and personalized medicine. Overall, AI represents a paradigm shift in pharmaceutical innovation, poised to make drug discovery faster, more precise, and more cost-effective while promoting a human–AI collaborative ecosystem for sustainable biomedical advancement.

Drug discovery remains a challenging task in both academic research and the pharmaceutical industry, owning to the fast upgrades to existing methods and the development of new methods that are applied in this field combined with the growing knowledge of disease mechanism from molecular biology. The speed with which a valuable new drug can be found and continue to use in clinics has accelerated rapidly compared to centuries ago. This thesis mainly focuses on the early stage of drug discovery aiming at human-diseases related protein targets. Four target proteins are introduced: PfPdxK kinase a protein related to malaria infections, human acute leukemia associated protein Menin and two human arginases involved in cancer. To investigate the interactions between proteins and ligands, we searched for and designed specific peptides, small fragments, or larger compounds to modulate or inhibit the protein targets. The designs were in part based on information gained from protein structures obtained by X-ray diffraction studies and we used fragment-based drug discovery and high throughput screening to screen large compound libraries to discover new binding compounds. Multiple biophysical approaches were used to validate the hits that were found, including differential scanning fluorimetry, microscale thermophoresis, and surface plasmon resonance. These techniques confirmed the hits found from screening and therefore provide a good start for fragment or compound growth index based improvement of the hits and later lead-like compounds optimization in future research.

Drug discovery research is a rational science aimed to identify new drugs to alleviate symptoms of numerous health problems. It takes around 10–15 years to develop a new drug with concerted efforts of multidisciplinary scientists. The cost of bringing a new drug to the market is approximately $2.6 billion. Only 10% of the promising drug candidates make it to the clinic. Rational drug design techniques have become very common in drug discovery research and expedite initial stage of screening. Artificial intelligence (AI) can exploit large chemical database from combinatorial chemistry and high-throughput screening to extract significant chemical and biological information to accelerate development of drugs and simultaneously cut down expenses of discovering new drugs. AI can assist new drug discovery in numerous ways like predicting targets for a disease and their three-dimensional structures for structure-based drug design, coming up with entirely novel chemical structure and its route of synthesis, improving reproducibility of results, accelerating repurposing of drugs for new therapeutic applications, prediction of toxicities, and other desired molecular properties in designed molecules, prioritization of potential drug candidate, and identifying privileged scaffolds. Several organizations like Merck, Schrodinger, Relay Therapeutic, and NVIDIA are investing in AI-driven drug discovery and development. This chapter will discuss in detail the vital role of AI in advancing various stages of drug discovery and development process and future directions.

Bioanalysis in a drug metabolism environment can be defined as the analytical techniques utilized to assay various samples that are the result ofin vitroorin vivostudies of one or more new chemical entities (NCEs). Bioanalysis has continued to evolve in response to the needs of the drug discovery and drug development teams, as well as the continuing improvement of the analytical technology that is used for the various assays. This chapter provides an overview of the various assays that are commonly used in various stages of drug discovery and drug development. The chapter is divided into three main sections: bioanalytical strategies,in vitroassays, andin vivoassays.For most of the assays discussed in this chapter, the analytical tool is high performance liquid chromatography (HPLC) combined with tandem mass spectrometry (MS/MS). It is common to have different levels of analytical acceptance criteria that become stricter as one moves from the early lead optimization arena into the development arena. By using this strategy, the higher throughput assays that are used in the screening stage would have minimal standards needed as part of the assay. As one goes to the higher levels, relatively smaller numbers of compounds are being assayed and the number of samples per compound is higher.There are multiplein vitroassays that are used for drug discovery screening of NCEs. The basic strategy for utilizing these variousin vitroassays is to use them to screen NCEs in order to select those that have druglike properties. These tests are commonly used as part of the absorption, distribution, metabolism, and excretion (ADME) optimization phase of new drug discovery. The most commonly used assays are designed to measure one of the following parameters: permeability, metabolic stability, enzyme inhibition, or metabolism. Each of these assay types are described in this chapter as a subtopics.In a drug discovery setting,in vivoassays are similar toin vitroassays in terms of the need for providing the results in a timely manner. The main difference betweenin vitroassays andin vivoassays is thatin vivoassays are often more challenging because the sample matrix is more complex. Mostin vivoassays rely on some form of HPLC‐MS/MS for the analytical step. The mainin vivotopics covered in this chapter are PK screening, PK studies, and metabolite identification.

Drug discovery is a complex, lengthy, and costly process with a high failure rate, especially during clinical trials. The integration of Artificial Intelligence (AI) has revolutionized various stages of drug discovery by enabling faster and more accurate analysis of biological and chemical data. However, most AI models in this field operate as “black boxes,” where their decision-making processes are opaque and difficult to interpret. This lack of transparency poses significant challenges in terms of trust, validation, and adoption of AI-generated predictions in both clinical and regulatory settings. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a promising approach to improve the interpretability of AI models without compromising their predictive power. This study aims to systematically review the opportunities, challenges, and future directions of XAI implementation in drug discovery. Using a qualitative method with a systematic literature review approach, data were collected from reputable databases including Scopus, PubMed, IEEE Xplore, SpringerLink, and ScienceDirect, focusing on publications from 2018 to 2024. The analysis identified five main themes: the role of XAI in molecular target identification, application of XAI in compound screening and molecular structure optimization, interpretation of drug toxicity predictions, challenges in XAI implementation, and future research directions. XAI techniques such as SHAP and LIME have proven useful in explaining AI model predictions, improving biological validation, and enabling more informed decision-making by scientists. However, significant challenges remain, including the trade-off between interpretability and accuracy, lack of universal standards, and the complexity of modeling biological systems. This study highlights the critical need for developing standardized interpretability frameworks, user-friendly interfaces, and collaborative environments between data scientists and healthcare professionals to foster XAI adoption in real-world drug discovery processes. Ultimately, XAI has the potential to increase transparency, trust, and efficiency, paving the way for safer and more effective therapeutic developments.

An alternative biomimetic tool – Dual hydrophilic/reversed-phase interaction mode

ABSTRACTPharmacokinetic studies play an important role in all stages of drug discovery and development. Recent advancements in the tools for discovery and optimization of therapeutic proteins have created an abundance of candidates that may fulfill target product profile criteria. Implementing a set of in silico, small scale in vitro and in vivo tools can help to identify a clinical lead molecule with promising properties at the early stages of drug discovery, thus reducing the labor and cost in advancing multiple candidates toward clinical development. In this review, we describe tools that should be considered during drug discovery, and discuss approaches that could be included in the pharmacokinetic screening part of the lead candidate generation process to de-risk unexpected pharmacokinetic behaviors of Fc-based therapeutic proteins, with an emphasis on monoclonal antibodies.

The present state and challenges of active learning in drug discovery

ABSTRACTThe most strategic weapon in drug discovery in the recent past has been artificial intelligence (AI)—bringing new approaches to one of the toughest areas of the pharmaceutical industry. Various AI approaches such as DL and ML methods utilized in various stages of drug discovery and development including but not limited to virtual screening and target identification are also discussed here. Employing this approach, this review looks at AI programs and platforms that exist in drug discovery today in a bid to outline what a future with AI in this field has in stock. In addition to this, this review does not only give a momentary state of the state of affairs of the AI in the space, but also briefly discusses what is in store next, along with the drawback and the opportunity more so from this perspective.

Harnessing machine learning for rational drug design.

Degenerative diseases, such as Alzheimer's, Parkinson's, diabetes, and cardiovascular disease, are global health problems with increasing prevalence and economic burden. This study aims to analyse the role of Artificial Intelligence (AI) in accelerating the drug discovery process, particularly in the development of targeted therapies for degenerative diseases. The research method used is a systematic literature review of national and international journal articles published between 2019 and 2024. The data were analysed qualitatively by examining the contribution of AI at various stages of drug discovery. The results of the study show that AI plays a significant role in molecular target identification, virtual screening, drug repurposing, and drug candidate optimisation, with a prediction accuracy rate ranging from 70–85%. The application of AI is reported to reduce drug discovery time by approximately 50% and development costs by up to 40%. The conclusions of this study confirm that AI is a strategic tool in modern pharmaceutical biotechnology development, although its implementation still requires further experimental validation, standardisation, and adequate data availability, particularly in the context of drug development in Indonesia

Biosensors based on the principle of surface plasmon resonance (SPR) are surface-sensitive optical devices used for monitoring biomolecular interactions at the sensor surface in real time without any labeling. It is used in a wide variety of areas including proteomics, clinical diagnosis, environmental monitoring, drug discovery, and food analysis. C-reactive protein (CRP) is a marker of inflammation, which undergoes conformation changes in local lesions, leading to the formation of mCRP. Autoantibodies against mCRP are frequently detected in systemic lupus erythematosus (SLE) and associated with disease activity and prognosis. An SPR immunoassay for CRP autoantibodies at complement factor H-CRP interface is described in this chapter.