Automating drug discovery

The Pistoia Alliance's methods database project: Instrument, chromatographic data system, and vendor-agnostic digital transfer of machine-readable high-performance liquid chromatography-ultraviolet methods using the allotrope data format

The Pistoia Alliance has successfully completed a pilot on the digital transfer of analytical High-Performance Liquid Chromatography (HPLC) methods and results between chromatography data systems (CDS) via a central data storage system using a standardized machine-readable data format, which transforms methods from paper documents to digital instructions. Critical method and result parameters in two example CDSs have been harmonized with a novel ontology and RDF-based graph data model created in this work. The authors will demonstrate how this new degree of data standardization can simplify data exchange day to day in the laboratory, and describe how the embedded semantics will position scientists to perform on-demand modern queries of data that has been automatically aggregated across vendor solutions. The developed solution was successfully tested at the analytical labs at Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA (MSD) and GSK, Stevenage, UK where there has been an effective transfer of HPLC information between different systems and sites to prove the concept and initial use cases. The work also begins to demonstrate the potential to realize many other use cases such as a series of critical improvements to current method transfer that eliminate the manual keying of data to reduce risk, steps, and error while improving overall flexibility.

Deciphering the landscape of allosteric glutaminase 1 inhibitors as anticancer agents

Glutamine is the second most utilised energy source after glucose for cancer cells to support their proliferation and survival. Glutaminase 1 (GLS1) is the rate-limiting enzyme during the glutaminolysis pathway and thus represents a promising therapeutic target for the development of innovative antitumor agents. Two main classes of GLS1 inhibitors, based on their different binding mode, are reported: the substrate active site and the allosteric site inhibitors. Despite the intense efforts made to date, only two GLS1 inhibitors (i.e.,CB-839 and IPN60090) have entered clinical trials. Therefore, this research field remains to be explored to improve the effectiveness of anticancer therapy. Hence, we describe the discovery and development of reversible allosteric GLS1 inhibitors disclosed in the last six years, dividing them based on their structural similarity with bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide (BPTES) and CB-839. Furthermore, macrocyclic and thiadiazole derivatives, and other structurally different compounds are discussed to present a wider picture of the chemical space under investigation. The study of the binding interactions governing GLS1 inhibition is also analyzed, to help prospectively refine the structural features for greater efficacy. Interestingly, an overview of a new class of irreversible allosteric inhibitors targeting GLS1 Lys320 key residue is provided for the first time. We also summarize the most important biological studies conducted on CB-839 and IPN60090 and their significance for further assessment. The insights garnered from this paper are expected to guide future drug design endeavours toward the identification of novel therapeutics targeting GLS1 to complement and potentially enhance the arsenal of anticancer medications.

Project automation in CCP4 Cloud: Enabling customization and high-throughput efficiency

The rapid advancement of automatic structure solution methods, driven by the availability of high-quality predicted structures from AlphaFold and the growing adoption of multi-crystal and serial experiments, has created a pressing need for streamlining routine operations, automating structure solution projects, and efficiently handling large volumes of data. Modern software solutions must be both robust and user-friendly, supporting manual workflows while enabling high-throughput operations to keep pace with the high data collection rates of modern beamlines. Here, we present new developments in CCP4 Cloud that address these challenges by providing predefined and customizable automatic workflows, which can be seamlessly integrated with experimental facilities, offering a powerful solution for modern macromolecular crystallography. CCP4 Cloud is available as a public service at https://cloud.ccp4.ac.uk.

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

Enhancing fluorescent probe design through multilayer interaction convolutional networks: advancing biosensing and bioimaging precision

Fluorescent probes are pivotal in biosensing and bioimaging, necessitating precise spectral tailoring for high-performance applications. Despite their importance, probe design remains largely empirical, a process that is both time-consuming and laborious. To streamline this, we created a comprehensive dataset of over 600 rhodamine fluorescent probes and employed a multilayer interaction convolutional model (MICNet) trained on molecular fingerprints to accurately predict excitation and emission wavelengths. Our model demonstrated high accuracy with mean relative errors (MRE) of 0.1% for excitation and 0.4% for emission wavelengths. Advancing this, we implemented a closed-loop strategy that integrates experimental feedback to iteratively enhance the design algorithm's accuracy, thereby improving the probes' performance and reliability. This method not only accelerates the probe development cycle but also facilitates the creation of spectrally customized fluorescent probes, offering a significant advancement in the field of bioanalytical chemistry.

Improving the Hit Rates of Virtual Screening by Active Learning from Bioactivity Feedback

Virtual screening has been widely used to identify potential hit candidates that can bind to the target protein in drug discovery. Contemporary screening methods typically rely on oversimplified scoring functions, frequently yielding one-digit hit rates (or even zero) among top-ranked candidates. The substantial cost of laboratory validation further constrains the exploration of candidate molecules. We find that test-time prediction refinement is almost blank in this area, which means bioactivity feedback in the wet-lab experiments is neglected. Here, we introduce an Active Learning from Bioactivity Feedback (ALBF) framework to enhance the weak hit rate of current virtual screening methods. ALBF spends the budget of wet-lab experiments iteratively and leverages the target-specific bioactivity insights from current wet-lab tests to refine the score results (i.e., rankings). Our framework consists of two components: a novel query strategy that considers the evaluation quality and its overall influence on other top-scored molecules; and an efficient score optimization strategy that propagates the bioactivity feedback to structurally similar molecules. We evaluated ALBF on diverse subsets of the well-known DUD-E and LIT-PCBA benchmarks. Our active learning protocol averagely enhances top-100 hit rates by 60% and 30% on DUD-E and LIT-PCBA with 50 to 200 bioactivity queries on the selected molecules that are deployed in ten rounds. The consistently superior performance demonstrates ALBF's potential to enhance both the accuracy and cost-effectiveness of active learning-based laboratory testing.

A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability

The major challenges in drug development stem from frequent structure-activity cliffs and unknown drug properties, which are expensive and time-consuming to estimate, contributing to a high rate of failures and substantial unavoidable costs in the clinical phases. Herein, we propose the self-conformation-aware graph transformer (SCAGE), an innovative deep learning architecture pretrained with approximately 5 million drug-like compounds for molecular property prediction. Notably, we develop a multitask pretraining framework, which incorporates four supervised and unsupervised tasks: molecular fingerprint prediction, functional group prediction using chemical prior information, 2D atomic distance prediction, and 3D bond angle prediction, covering aspects from molecular structures to functions. It enables learning comprehensive conformation-aware prior knowledge, thereby enhancing its generalization across various molecular property tasks. Moreover, we design a data-driven multiscale conformational learning strategy that effectively guides the model in understanding and representing atomic relationships at the molecular conformational scale. SCAGE achieves significant performance improvements across 9 molecular properties and 30 structure-activity cliff benchmarks. Case studies demonstrate that SCAGE accurately captures crucial functional groups at the atomic level, which are closely associated with molecular activity, providing valuable insights into quantitative structure-activity relationships.

The efficiency of high-throughput screening (HTS) and in-silico data analysis during medical emergencies: Identification of effective antiviral 3CLpro inhibitors

The COVID-19 pandemic highlighted the importance of accelerating the drug discovery process. The 3-chymotrypsin-like protease (3CLpro) is a critical enzyme in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral replication process and was quickly identified as a prime target for drug development. This study leverages High-Throughput Screening (HTS) to identify novel 3CLpro inhibitors. We screened a different library of 325,000 compounds, leading to the discovery of two new chemical scaffolds with selective inhibitory activity against 3CLpro. In-silico analysis and further experimental validation, elucidated the binding modes and mechanisms of action, revealing a covalent inhibitor targeting the catalytic pocket and two allosteric inhibitors affecting the monomer/dimer equilibrium of 3CLpro. The identified compounds demonstrated significant antiviral activity in vitro, reducing SARS-CoV-2 replication in VeroE6 and Calu-3 cell lines. This study highlights the potential of combining HTS and computational approaches to accelerate the discovery of effective antiviral agents, suggesting a workflow to support the research and the design of effective therapeutic strategies.

Rare Disease Policy in High-Income Countries: An Overview of Achievements, Challenges, and Solutions

OBJECTIVES

To provide an overview of policy initiatives in high-income countries aimed at supporting the development and accessibility of treatments for rare diseases.

METHODS

We examine how legislative, research, and pricing policies in high-income countries address barriers that have historically hindered innovation and access to rare disease treatments. By analyzing examples from the European Union, United Kingdom, United States, Canada, Japan, and Australia, the article identifies ongoing initiatives, outlines current challenges, and explores proposed solutions to foster a sustainable, innovative, and accessible rare disease treatment ecosystem.

RESULTS

The review highlights policies such as legislative incentives in the European Union, United States, and Japan for orphan drug development, public-private partnerships to boost innovation, and patient registries to support research and clinical trials. Despite these efforts, major challenges persist, including high therapy costs, limited access to innovation for ultrarare diseases, and diagnostic delays, with significant disparities across regions.

CONCLUSIONS

Overcoming these challenges will require sustainable pricing and reimbursement frameworks, alongside stronger collaboration between stakeholders, particularly for ultrarare diseases. Advanced technologies, such as artificial intelligence, hold promise for improving diagnostic accuracy and data collection, supported by enhanced coding systems and registries to facilitate more robust research.

Nano-enabled pharmacogenomics: revolutionizing personalized drug therapy

The combination of pharmacogenomics and nanotechnology science of pharmacogenomics into a highly advanced single entity has given birth to personalized medicine known as nano-enabled pharmacogenomics. This review article covers all aspects starting from pharmacogenomics to gene editing tools, how these have evolved or are likely to be evolved for pharmacogenomic application, and how these can be delivered using nanoparticle delivery systems. In this prior work, we explore the evolution of pharmacogenomics over the years, as well as new achievements in the field of genomic sciences, the challenges in drug creation, and application of the strategy of personalized medicine. Particular attention is paid to how nanotechnology helps avoid the problems that accompanied the development of pharmacogenomics earlier, for example, the question of drug resistance and targeted delivery. We also review the latest developments in nano-enabled pharmacogenomics, such as the coupling with other nanobio-technologies, artificial intelligence, and machine learning in pharmacogenomics, and the ethical and regulatory aspects of these developing technologies. The possible uses of nanotechnology in improving the chances of pated and treating drug-resistant cancers are exemplified by case studies together with the current clinical uses of nanotechnology. In the last section, we discuss the future trends and research prospects in this dynamically growing area, stressing the importance of further advancements and collaborations which will advance the nano-enabled pharmacogenomics to their maximum potential.

High-throughput microdroplet-based synthesis using automated array-to-array transfer

Automation of chemical synthesis and high-throughput (HT) screening are important for speeding up drug discovery. Here, we describe an automated HT picomole scale synthesis system which uses desorption electrospray ionization (DESI) to create microdroplets of reaction mixtures at individual positions from a two-dimensional reactant array and transfer them to a corresponding position in an array of collected reaction products. On-the-fly chemical transformations are facilitated by the reaction acceleration phenomenon in microdroplets and high reaction conversions are achieved during the milliseconds droplet flight time from the reactant to the product array. Successful functionalization of bioactive molecules is demonstrated through the generation of 172 analogs (64% success rate) using multiple reaction types. Synthesis throughput is ∼45 seconds/reaction including droplet formation, reaction, and collection steps, all of which occur in an integrated fashion, generating product amounts sufficient for subsequent bioactivity screening (low ng to low μg). Quantitative performance was validated using LC/MS. This system bridges the demonstrated capabilities of HT-DESI for reaction screening and label-free bioassays, allowing consolidation of the key early drug discovery steps around a single synthetic-analytical technology.

E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays

Assay interference caused by small organic compounds continues to pose formidable challenges to early drug discovery. Various computational methods have been developed to identify compounds likely to cause assay interference. However, due to the scarcity of data available for model development, the predictive accuracy and applicability of these approaches are limited. In this work, we present E-GuARD, a novel framework seeking to address data scarcity and imbalance by integrating self-distillation, active learning, and expert-guided molecular generation. E-GuARD iteratively enriches the training data with interference-relevant molecules, resulting in quantitative structure-interference relationship (QSIR) models with superior performance. We demonstrate the utility of E-GuARD with the examples of four high-quality data sets on thiol reactivity, redox reactivity, nanoluciferase inhibition, and firefly luciferase inhibition. Our models reached MCC values of up to 0.47 for these data sets, with two-fold or higher improvements in enrichment factors compared to models trained without E-GuARD data augmentation. These results highlight the potential of E-GuARD as a scalable solution to mitigating assay interference in early drug discovery. SCIENTIFIC CONTRIBUTION: We present E-GuARD, an innovative framework that combines iterative self-distillation with guided molecular augmentation to enhance the predictive performance of QSAR models. By allowing models to learn from newly generated, informative compounds through iterations, E-GuARD facilitates the understanding of underrepresented structural patterns and improves performance on unseen data. When applied across different interference mechanisms, E-GuARD consistently outperformed standard approaches. E-GuARD establishes the foundation for further research into dynamic data enrichment and more robust molecular modeling.

High-Performance Liquid Chromatographic Separation of Stereoisomers of ß-Methyl-Substituted Unusual Amino Acids Utilizing Ion Exchangers Based on Cinchona Alkaloids

Novel peptides based on common amino acid building blocks may serve as possible drug candidates; however, their flexible structures may require stabilization via the incorporation of conformational constraints. The insertion of unusual amino acids is a feasible option that may provide improved pharmacokinetic and pharmacodynamic properties of such peptide-type drugs. The stereochemical purity of these kinds of building blocks must be verified by an efficient separation technique, such as high-performance liquid chromatography. Here, we present and discuss the results of the stereoselective separation mechanism of ß-methylated phenylalanine (ß-MePhe), tyrosine (ß-MeTyr), 1,2,3,4-tetrahydroisoquinoline-3-carboxylic acid (ß-MeTic), and cyclohexylalanine (ß-MeCha) together with non-methylated Phe, Tyr, Tic, and Cha applying Cinchona alkaloid-based chiral stationary phases (CSPs). The studied zwitterionic CSPs acting as ion exchangers provided optimal performance in the polar ionic mode when methanol or a mixture of methanol and acetonitrile was utilized as the mobile phase together with organic acid and base additives. It was found that the basicity of small amines applied as mobile phase additives did not directly influence the chromatographic ion exchange concept. However, the size of the amines and their concentration led to a reduced retention time following the principles of ion exchange chromatography. On the basis of a systematic study of the effects of the eluent composition on the chromatographic behavior, important structure-retention and enantioselectivity relationships could be revealed. Through a temperature study, it has become evident that the composition of the eluent and the structure of analytes markedly affect the thermodynamic properties.

Unveiling Dynamic Hotspots in Protein-Ligand Binding: Accelerating Target and Drug Discovery Approaches

Computational methods have transformed target and drug discovery, significantly accelerating the identification of biological targets and lead compounds. Despite its limitations, in silico molecular docking represents a foundational tool. Molecular Dynamics (MD) simulations, employing accurate force fields, provide near-realistic insights into a compound's behavior within a biological target. However, docking and MD predictions may be unreliable without precise knowledge of the target binding site. Through MD simulations, we investigated 100 co-crystal structures of biological targets complexed with active compounds, identifying key structural and energy dynamic features that govern target-ligand interactions. Our analysis provides a detailed quantitative description of these parameters, offering critical validation for improving the predictive reliability of docking and MD simulations. This work provides a robust framework for refining early-stage drug discovery and target identification.

Closed-Loop Navigation of a Kinetic Zone Diagram for Redox-Mediated Electrocatalysis Using Bayesian Optimization, a Digital Twin, and Automated Electrochemistry

Molecular electrocatalysis campaigns often require tuning multiple experimental parameters to obtain kinetically insightful electrochemical measurements, a prohibitively time-consuming task when performing comprehensive studies across multiple catalysts and substrates. In this work, we present an autonomous workflow that combines Bayesian optimization and automated electrochemistry to perform fully unsupervised cyclic voltammetry (CV) studies of molecular electrocatalysis. We developed CV descriptors that leveraged the conceptual framework of the EC' (where EC' denotes an electrochemical step followed by a catalytic chemical step) kinetic zone diagram to enable efficient Bayesian optimization. The CV descriptor's effect on optimization performance was evaluated using a digital twin of our autonomous experimental platform, quantifying the accuracy of obtained kinetic values against the known ground truth. We demonstrated our platform experimentally by performing autonomous studies of TEMPO-catalyzed ethanol and isopropanol electro-oxidation, demonstrating rapid identification of kinetically insightful conditions in 10 or less iterations through the closed-loop workflow. Overall, this work highlights the application of autonomous electrochemical platforms to accelerate mechanistic studies in molecular electrocatalysis and beyond.

A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors

Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer-aided drug discovery, it is crucial to utilize effective molecular feature representations for predicting molecular properties and designing ligands with high binding affinity to targets. However, designing an effective multi-task and self-supervised strategy remains a significant challenge for the pretraining framework. In this study, a multi-task self-supervised deep learning framework is proposed, MTSSMol, which utilizes ≈10 million unlabeled drug-like molecules for pretraining to identify potential inhibitors of fibroblast growth factor receptor 1 (FGFR1). During the pretraining of MTSSMol, molecular representations are learned through a graph neural networks (GNNs) encoder. A multi-task self-supervised pretraining strategy is proposed to fully capture the structural and chemical knowledge of molecules. Extensive computational tests on 27 datasets demonstrate that MTSSMol exhibits exceptional performance in predicting molecular properties across different domains. Moreover, MTSSMol's capability is validated to identify potential inhibitors of FGFR1 through molecular docking using RoseTTAFold All-Atom (RFAA) and molecular dynamics simulations. Overall, MTSSMol provides an effective algorithmic framework for enhancing molecular representation learning and identifying potential drug candidates, offering a valuable tool to accelerate drug discovery processes. All of the codes are freely available online at https:// github.com/zhaoqi106/MTSSMol.

Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools

Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen-antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes.

Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets

The control of socioeconomically important parasitic roundworms (nematodes) of animals has become challenging or ineffective due to problems associated with widespread resistance in these worms to most classes of chemotherapeutic drugs (anthelmintics) currently available. Thus, there is an urgent need to discover and develop novel compounds with unique mechanisms of action to underpin effective parasite control programmes. Here, we evaluated an in silico (computational) approach to accelerate the discovery of new anthelmintics against the parasitic nematode Haemonchus contortus (barber's pole worm) as a model system. Using a supervised machine learning workflow, we trained and assessed a multi-layer perceptron classifier on a labelled dataset of 15,000 small-molecule compounds, for which extensive bioactivity data were previously obtained for H. contortus via high-throughput screening, as well as evidence-based datasets from the peer-reviewed literature. This model achieved 83% precision and 81% recall on the class of 'active' compounds during testing, despite a high imbalance in the training data, with only 1% of compounds carrying this label. The trained model was then used to infer nematocidal candidates by in silico screening of 14.2 million compounds from the ZINC15 database. An experimental assessment of 10 of these candidates showed significant inhibitory effects on the motility and development of H. contortus larvae and adults in vitro, with two compounds exhibiting high potency for further exploration as lead candidates. These findings indicate that the present machine learning-based approach could accelerate the in silico prediction and prioritisation of anthelmintic small molecules for subsequent in vitro and in vivo validations.

Integrating artificial intelligence in drug discovery and early drug development: a transformative approach

Artificial intelligence (AI) can transform drug discovery and early drug development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, and low success rates. In this review we provide an overview of how to integrate AI to the current drug discovery and development process, as it can enhance activities like target identification, drug discovery, and early clinical development. Through multiomics data analysis and network-based approaches, AI can help to identify novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold, predict protein structures with high accuracy, aiding druggability assessments and structure-based drug design. AI also facilitates virtual screening and de novo drug design, creating optimized molecular structures for specific biological properties. In early clinical development, AI supports patient recruitment by analyzing electronic health records and improves trial design through predictive modeling, protocol optimization, and adaptive strategies. Innovations like synthetic control arms and digital twins can reduce logistical and ethical challenges by simulating outcomes using real-world or virtual patient data. Despite these advancements, limitations remain. AI models may be biased if trained on unrepresentative datasets, and reliance on historical or synthetic data can lead to overfitting or lack generalizability. Ethical and regulatory issues, such as data privacy, also challenge the implementation of AI. In conclusion, in this review we provide a comprehensive overview about how to integrate AI into current processes. These efforts, although they will demand collaboration between professionals, and robust data quality, have a transformative potential to accelerate drug development.

Active learning and Gaussian processes for the development of dissolution models: An AI-based data-efficient approach

In vitro dissolution testing plays a key role in controlling the quality and optimizing the formulation of solid dosage pharmaceutical products. Data-driven dissolution models can improve the efficiency of testing: their predictions can act as surrogates to physical experiments and help identify key material attributes / processing parameters that impact product dissolution. Reducing the data (size) requirements of developing such models would significantly improve the utility of dissolution models. In this study, we investigate how Gaussian process regression (GPR) and active learning can reduce the dataset-size requirements for developing predictive models and identifying important processing parameters compared to common practice methods. Initially, we perform a DoE study over five processing parameters and measure the dissolution of compound B to generate a dataset. Using this dataset, we find that GPR provides higher fidelity predictions of dissolution than polynomial models when trained on the same data. In addition, we use Shapley additive explanations to interpret our GPR model and assess processing parameter importance. Through a retrospective analysis, we find that active learning can target a reduced set of experiments (compared to a full DoE) which are particularly conducive for model development and identification of important processing parameters.

Toward target 2035: EUbOPEN - a public-private partnership to enable & unlock biology in the open

Target 2035 is a global initiative that seeks to identify a pharmacological modulator of most human proteins by the year 2035. As part of an ongoing series of annual updates of this initiative, we summarise here the efforts of the EUbOPEN project whose objectives and results are making a strong contribution to the goals of Target 2035. EUbOPEN is a public-private partnership with four pillars of activity: (1) chemogenomic library collections, (2) chemical probe discovery and technology development for hit-to-lead chemistry, (3) profiling of bioactive compounds in patient-derived disease assays, and (4) collection, storage and dissemination of project-wide data and reagents. The substantial outputs of this programme include a chemogenomic compound library covering one third of the druggable proteome, as well as 100 chemical probes, both profiled in patient derived assays, as well as hundreds of data sets deposited in existing public data repositories and a project-specific data resource for exploring EUbOPEN outputs.

Rational Design, Synthesis, and Biological Evaluation of Novel c-Met Degraders for Lung Cancer Therapy

Cellular-mesenchymal epithelial transition factor (c-Met) is an attractive target for treating multiple cancers. Despite plentiful c-Met inhibitors have been developed, some issues, including the acquired drug resistance to c-Met inhibitors, have emerged to hamper their application in clinical treatment. Degradation of c-Met offers an opportunity to solve these issues. In this study, we developed a series of c-Met degraders, and the optimal compound 22b can efficiently degrade c-Met with a DC50 value of 0.59 nM in EBC-1 cells. Mechanistic studies revealed that compound 22b induced c-Met degradation via proteasome-mediated pathway. In addition, compound 22b suppressed the proliferation and also induced apoptosis of EBC-1 cells, outperforming the corresponding inhibitor tepotinib. Importantly, compound 22b showed favorable pharmacokinetic properties and significantly induced tumor regression in a xenograft model without obvious toxicity. In brief, this study provided compound 22b as a novel c-Met degrader for lung cancer therapy.

A review of large language models and autonomous agents in chemistry

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accelerate scientific discovery through automation. We also review LLM-based autonomous agents: LLMs with a broader set of tools to interact with their surrounding environment. These agents perform diverse tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry and discuss across any scientific domains. This review covers the recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. Key challenges include data quality and integration, model interpretability, and the need for standard benchmarks, while future directions point towards more sophisticated multi-modal agents and enhanced collaboration between agents and experimental methods. Due to the quick pace of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.

Autonomous Nucleic Acid and Protein Nanocomputing Agents Engineered to Operate in Living Cells

In recent years, the rapid development and employment of autonomous technology have been observed in many areas of human activity. Autonomous technology can readily adjust its function to environmental conditions and enable an efficient operation without human control. While applying the same concept to designing advanced biomolecular therapies would revolutionize nanomedicine, the design approaches to engineering biological nanocomputing agents for predefined operations within living cells remain a challenge. Autonomous nanocomputing agents made of nucleic acids and proteins are an appealing idea, and two decades of research has shown that the engineered agents act under real physical and biochemical constraints in a logical manner. Throughout all domains of life, nucleic acids and proteins perform a variety of vital functions, where the sequence-defined structures of these biopolymers either operate on their own or efficiently function together. This programmability and synergy inspire massive research efforts that utilize the versatility of nucleic and amino acids to encode functions and properties that otherwise do not exist in nature. This Perspective covers the key concepts used in the design and application of nanocomputing agents and discusses potential limitations and paths forward.

Electrochemical C-O and C-N Arylation using Alternating Polarity in flow for Compound Libraries

Etherification and amination of aryl halide scaffolds are commonly used reactions in parallel medicinal chemistry to rapidly scan structure-activity relationships with abundant building blocks. Electrochemical methods for aryl etherification and amination demonstrate broad functional group tolerance and extended nucleophile scope compared to traditional methods. Nevertheless, there is a need for robust and scale-transferable workflows for electrochemical compound library synthesis. Herein we describe a platform for automated electrochemical synthesis of C-X arylation (X=NH, OH) in flow to access compound libraries. A comprehensive Design of Experiment (DoE) study identifies an optimal protocol which generates high yields across>30 aryl halide scaffolds, diverse amines (including electron-deficient sulfonamides, sulfoximines, amides, and anilines) and alcohols (including serine residues within peptides). Reaction sequences are automated on commercially available equipment to generate libraries of anilines and aryl ethers. The unprecedented application of potentiostatic alternating polarity in flow is essential to avoid accumulating electrode passivation. Moreover, it enables reactions to be performed in air, without supporting electrolyte and with high reproducibility over consecutive runs. Our method represents a powerful means to rapidly generate nucleophile independent C-X arylation compound libraries using flow electrochemistry.

Simple User-Friendly Reaction Format

Utilizing the growing wealth of chemical reaction data can boost synthesis planning and increase success rates. Yet, the effectiveness of machine learning tools for retrosynthesis planning and forward reaction prediction relies on accessible, well-curated data presented in a structured format. Although some public and licensed reaction databases exist, they often lack essential information about reaction conditions. To address this issue and promote the principles of findable, accessible, interoperable, and reusable (FAIR) data reporting and sharing, we introduce the Simple User-Friendly Reaction Format (SURF). SURF standardizes the documentation of reaction data through a structured tabular format, requiring only a basic understanding of spreadsheets. This format enables chemists to record the synthesis of molecules in a format that is understandable by both humans and machines, which facilitates seamless sharing and integration directly into machine learning pipelines. SURF files are designed to be interoperable, easily imported into relational databases, and convertible into other formats. This complements existing initiatives like the Open Reaction Database (ORD) and Unified Data Model (UDM). At Roche, SURF plays a crucial role in democratizing FAIR reaction data sharing and expediting the chemical synthesis process.

Deep Bayesian active learning using in-memory computing hardware

Labeling data is a time-consuming, labor-intensive and costly procedure for many artificial intelligence tasks. Deep Bayesian active learning (DBAL) boosts labeling efficiency exponentially, substantially reducing costs. However, DBAL demands high-bandwidth data transfer and probabilistic computing, posing great challenges for conventional deterministic hardware. Here we propose a memristor stochastic gradient Langevin dynamics in situ learning method that uses the stochastic of memristor modulation to learn efficiency, enabling DBAL within the computation-in-memory (CIM) framework. To prove the feasibility and effectiveness of the proposed method, we implemented in-memory DBAL on a memristor-based stochastic CIM system and successfully demonstrated a robot's skill learning task. The inherent stochastic characteristics of memristors allow a four-layer memristor Bayesian deep neural network to efficiently identify and learn from uncertain samples. Compared with cutting-edge conventional complementary metal-oxide-semiconductor-based hardware implementation, the stochastic CIM system achieves a remarkable 44% boost in speed and could conserve 153 times more energy.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

Status and Prospects of Research on Deep Learning-based De Novo Generation of Drug Molecules

Traditional molecular de novo generation methods, such as evolutionary algorithms, generate new molecules mainly by linking existing atomic building blocks. The challenging issues in these methods include difficulty in synthesis, failure to achieve desired properties, and structural optimization requirements. Advances in deep learning offer new ideas for rational and robust de novo drug design. Deep learning, a branch of machine learning, is more efficient than traditional methods for processing problems, such as speech, image, and translation. This study provides a comprehensive overview of the current state of research in de novo drug design based on deep learning and identifies key areas for further development. Deep learning-based de novo drug design is pivotal in four key dimensions. Molecular databases form the basis for model training, while effective molecular representations impact model performance. Common DL models (GANs, RNNs, VAEs, CNNs, DMs) generate drug molecules with desired properties. The evaluation metrics guide research directions by determining the quality and applicability of generated molecules. This abstract highlights the foundational aspects of DL-based de novo drug design, offering a concise overview of its multifaceted contributions. Consequently, deep learning in de novo molecule generation has attracted more attention from academics and industry. As a result, many deep learning-based de novo molecule generation types have been actively proposed.

Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence

Alzheimer's disease (AD) is a multidimensional, complex condition that affects individuals all over the world. Despite decades of experimental and clinical research that has revealed various processes, many concerns concerning the origin of Alzheimer's disease remain unresolved. Despite the notion that there isn't a complete set of jigsaw pieces, the growing number of public data-sharing initiatives that collect biological, clinical, and lifestyle data from those suffering from Alzheimer's disease has resulted in virtually endless volumes of knowledge about the disorder, far beyond what humans can comprehend. Furthermore, combining Big Data from multi- -omics research gives a chance to investigate the pathophysiological processes underlying the whole biological spectrum of Alzheimer's disease. To improve knowledge on the subject of Alzheimer's disease, Artificial Intelligence (AI) offers a wide variety of approaches for evaluating complex and significant data. The introduction of next-generation sequencing and microarray technologies has resulted in significant growth in genetic data research. When it comes to assessing such complex projects, AI technology beats conventional statistical techniques of data processing. This review focuses on current research and potential challenges for AI in Alzheimer's disease research. This article, in particular, examines how AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies. Ultimately, scientists want to develop individualized, efficient medicines.

Improving generalizability of drug-target binding prediction by pre-trained multi-view molecular representations

MOTIVATION

Most drugs start on their journey inside the body by binding the right target proteins. This is the reason that numerous efforts have been devoted to predicting the drug-target binding during drug development. However, the inherent diversity among molecular properties, coupled with limited training data availability, poses challenges to the accuracy and generalizability of these methods beyond their training domain.

RESULTS

In this work, we proposed a neural networks construction for high accurate and generalizable drug-target binding prediction, named Pre-trained Multi-view Molecular Representations (PMMR). The method uses pre-trained models to transfer representations of target proteins and drugs to the domain of drug-target binding prediction, mitigating the issue of poor generalizability stemming from limited data. Then, two typical representations of drug molecules, Graphs and SMILES strings, are learned respectively by a Graph Neural Network and a Transformer to achieve complementarity between local and global features. PMMR was evaluated on drug-target affinity and interaction benchmark datasets, and it derived preponderant performance contrast to peer methods, especially generalizability in cold-start scenarios. Furthermore, our state-of-the-art method was indicated to have the potential for drug discovery by a case study of cyclin-dependent kinase 2.

AVAILABILITY AND IMPLEMENTATION

https://github.com/NENUBioCompute/PMMR.

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Traditional methods of drug discovery are often time-consuming, expensive, and characterized by low success rates. Because of this, there is an urgent need to improve the drug development process using new technologies. The integration of the current state-of-art of artificial intelligence (AI) and machine learning (ML) approaches with conventional methods will enhance the efficiency and effectiveness of pharmaceutical research. This review highlights the transformative impact of AI and ML in drug discovery, discussing current applications, challenges, and future directions in harnessing these technologies to accelerate the development of innovative therapeutics. We have discussed the latest developments in AI and ML technologies to streamline several stages of drug discovery, from target identification and validation to lead optimization and preclinical studies.

Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery

The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literature (past decade) of case studies where such strategies have been applied to accelerate the discovery pipeline. Compared to 50 years ago when drug discovery was largely a synthetic chemist driven research exercise, today a holistic approach needs to be adopted with seamless integration between synthetic and medicinal chemistry, supramolecular complexes, computations, artificial intelligence, machine learning, structural biology, chemical biology, diffraction analytical tools, drugs databases, and pharmacology. The urgency for an integrated and collaborative platform to accelerate drug discovery in an academic setting is emphasized.

Strategic partnerships for AI-driven drug discovery: The role of relational dynamics

Artificial intelligence-driven drug discovery (AIDD) companies hold significant promise for transforming pharmaceutical development, yet little is known about how they manage partnerships with established pharmaceutical firms. To address this research gap, our study explores how AIDD companies develop and leverage relational capabilities to enhance collaboration effectiveness. Through a case study approach, we focus on four key relational aspects: identifying complementary capabilities, establishing effective governance mechanisms, creating relationship-specific assets, and developing interfirm knowledge-sharing routines. Our findings demonstrate that particularly effective governance of intellectual property is essential for partnership success. We offer actionable recommendations for AIDD companies to strengthen collaborations, thereby contributing to the realization of AI's potential in drug discovery.

Accurate and transferable drug-target interaction prediction with DrugLAMP

MOTIVATION

Accurate prediction of drug-target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities.

RESULTS

We introduce DrugLAMP (PLM-assisted multi-modal prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction. DrugLAMP integrates molecular graph and protein sequence features extracted by PLMs and traditional feature extractors. We introduce two novel multi-modal fusion modules: (i) pocket-guided co-attention (PGCA), which uses protein pocket information to guide the attention mechanism on drug features, and (ii) paired multi-modal attention (PMMA), which enables effective cross-modal interactions between drug and protein features. These modules work together to enhance the model's ability to capture complex drug-protein interactions. Moreover, the contrastive compound-protein pre-training (2C2P) module enhances the model's generalization to real-world scenarios by aligning features across modalities and conditions. Comprehensive experiments demonstrate DrugLAMP's state-of-the-art performance on both standard benchmarks and challenging settings simulating real-world drug discovery, where test drugs/targets are unseen during training. Visualizations of attention maps and application to predict cryptic pockets and drug side effects further showcase DrugLAMP's strong interpretability and generalizability. Ablation studies confirm the contributions of the proposed modules.

AVAILABILITY AND IMPLEMENTATION

Source code and datasets are freely available at https://github.com/Lzcstan/DrugLAMP. All data originate from public sources.

Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.

Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1

BACKGROUND

Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches.

RESULTS

We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development.

CONCLUSION

This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5's HydraScreen and Strateos' automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs.

SCIENTIFIC CONTRIBUTION

We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.

Targeting the GTPase RAN by liposome delivery for tackling cancer stemness-emanated therapeutic resistance

Cancer therapeutic resistance as a common hallmark of cancer is often responsible for treatment failure and poor patient survival. Cancer stem-like cells (CSCs) are one of the main contributors to therapeutic resistance, cancer relapse and metastasis. Through screening from our in-house library of natural products, we found polyphyllin II (PPII) as a potent anti-CSC compound for triple-negative breast cancer (TNBC). To enhance anti-CSC selectivity and improve druggability of PPII, we leverage the liposome-mediated delivery technique for increasing solubility of PPII, and more significantly, attaining broader therapeutic window. Liposomal PPII demonstrates its marked potency to inhibit tumor growth, post-surgical recurrence and metastasis compared to commercial liposomal chemotherapeutics in the mouse models of CSC-enriched TNBC tumor. We further identify PPII as an inhibitor of the Ras-related nuclear (RAN) protein whose upregulated expression is correlated with poor clinical outcomes. The direct binding of PPII to RAN reduces TNBC stemness, thereby suppressing tumor progression. Our work offers a significance from drug discovery to drug delivery benefiting from liposome technique for targeted treatment of high-stemness tumor.

Pyrazolo-Pyrimidinones with Improved Solubility and Selective Inhibition of Adenylyl Cyclase Type 1 Activity for Treatment of Inflammatory Pain

Adenylyl cyclase isoform 1 (AC1) is considered a promising target for treating inflammatory pain. Our group identified the pyrazolyl-pyrimidinone scaffold as potent and selective inhibitors of Ca2+/CaM-mediated AC1 activity; however, the molecules suffered from poor aqueous solubility. The current study presents a strategy to improve aqueous solubility of the scaffold by reduction of crystal packing energy and increasing rotational degrees of freedom within the molecule. Structure-activity and property relationship studies identified the second generation lead 7-47A (AC10142A) that demonstrated and AC1 IC50 value of 0.26 μM and aqueous solubility of 74 ± 7 μM. After in vitro ADME characterization, the scaffold advanced to in vivo pharmacokinetic evaluation, demonstrating adequate levels of exposure. Finally, 7-47A exhibited antiallodynic efficacy in a rat complete Freund's adjuvant model for inflammatory pain showing improvement over previous iterations of this scaffold. These results further validate AC1 inhibition as a viable therapeutic strategy for treating chronic and inflammatory pain.

Deciphering Molecular Embeddings with Centered Kernel Alignment

Analyzing machine learning models, especially nonlinear ones, poses significant challenges. In this context, centered kernel alignment (CKA) has emerged as a promising model analysis tool that assesses the similarity between two embeddings. CKA's efficacy depends on selecting a kernel that adequately captures the underlying properties of the compared models. The model analysis tool was designed for neural networks (NNs) with their invariance to data rotation in mind and has been successfully employed in various scientific domains. However, CKA has rarely been adopted in cheminformatics, partly because of the popularity of the random forest (RF) machine learning algorithm, which is not rotationally invariant. In this work, we present the adaptation of CKA that builds on the RF kernel to match the properties of RF. As part of the method validation, we show that the model analysis method is well-correlated with the prediction similarity of RF models. Furthermore, we demonstrate how CKA with the RF kernel can be utilized to analyze and explain the behavior of RF models derived from molecular and rooted fingerprints.

Current Updates of CRISPR/Cas System and Anti-CRISPR Proteins: Innovative Applications to Improve the Genome Editing Strategies

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated sequence (CRISPR/Cas) system is a cutting-edge genome-editing tool employed to explore the functions of normal and disease-related genes. The CRISPR/Cas system has a remarkable diversity in the composition and architecture of genomic loci and Cas protein sequences. Owing to its excellent efficiency and specificity, this system adds an outstanding dimension to biomedical research on genetic manipulation of eukaryotic cells. However, safe, efficient, and specific delivery of this system to target cells and tissues and their off-target effects are considered critical bottlenecks for the therapeutic applications. Recently discovered anti-CRISPR proteins (Acr) play a significant role in limiting the effects of this system. Acrs are relatively small proteins that are highly specific to CRISPR variants and exhibit remarkable structural diversity. The in silico approaches, crystallography, and cryo-electron microscopy play significant roles in elucidating the mechanisms of action of Acrs. Acrs block the CRISPR/Cas system mainly by employing four mechanisms: CRISPR/Cas complex assembly interruption, target-binding interference, target cleavage prevention, and degradation of cyclic oligonucleotide signaling molecules. Engineered CRISPR/Cas systems are frequently used in gene therapy, diagnostics, and functional genomics. Understanding the molecular mechanisms underlying Acr action may help in the safe and effective use of CRISPR/Cas tools for genetic modification, particularly in the context of medicine. Thus, attempts to regulate prokaryotic CRISPR/Cas surveillance complexes will advance the development of antimicrobial drugs and treatment of human diseases. In this review, recent updates on CRISPR/Cas systems, especially CRISPR/Cas9 and Acrs, and their novel mechanistic insights are elaborated. In addition, the role of Acrs in the novel applications of CRISPP/Cas biotechnology for precise genome editing and other applications is discussed.

Design considerations when creating a high throughput screen-compatible in vitro model of osteogenesis

Inducing osteogenic differentiation in vitro is useful for the identification and development of bone regeneration therapies as well as modelling bone disorders. To couple in vitro models with high throughput screening techniques retains the assay's relevance in research while increasing its therapeutic impact. Miniaturizing, automating and/or digitalizing in vitro assays will reduce the required quantity of cells, biologic stimulants, culture/output assay reagents, time and cost. This review highlights the design and workflow considerations for creating a high throughput screen-compatible model of osteogenesis, comparing and contrasting osteogenic cell type, assay fabrication and culture methodology, osteogenic induction approach and repurposing existing output techniques.

AEcroscopy: A Software-Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software-hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions

Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.

Hit me with your best shot: Integrated hit discovery for the next generation of drug targets

Identification of high-quality hit chemical matter is of vital importance to the success of drug discovery campaigns. However, this goal is becoming ever harder to achieve as the targets entering the portfolios of pharmaceutical and biotechnology companies are increasingly trending towards novel and traditionally challenging to drug. This demand has fuelled the development and adoption of numerous new screening approaches, whereby the contemporary hit identification toolbox comprises a growing number of orthogonal and complementary technologies including high-throughput screening, fragment-based ligand design, affinity screening (affinity-selection mass spectrometry, differential scanning fluorimetry, DNA-encoded library screening), as well as increasingly sophisticated computational predictive approaches. Herein we describe how an integrated strategy for hit discovery, whereby multiple hit identification techniques are tactically applied, selected in the context of target suitability and resource priority, represents an optimal and often essential approach to maximise the likelihood of identifying quality starting points from which to develop the next generation of medicines.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

A comprehensive review on organ-on-chips as powerful preclinical models to study tissue barriers

In the preclinical stage of drug development, 2D and 3D cell cultures under static conditions followed by animal models are utilized. However, these models are insufficient to recapitulate the complexity of human physiology. With the developing organ-on-chip (OoC) technology in recent years, human physiology and pathophysiology can be modeled better than traditional models. In this review, the need for OoC platforms is discussed and evaluated from both biological and engineering perspectives. The cellular and extracellular matrix components are discussed from a biological perspective, whereas the technical aspects such as the intricate working principles of these systems, the pivotal role played by flow dynamics and sensor integration within OoCs are elucidated from an engineering perspective. Combining these two perspectives, bioengineering applications are critically discussed with a focus on tissue barriers such as blood-brain barrier, ocular barrier, nasal barrier, pulmonary barrier and gastrointestinal barrier, featuring recent examples from the literature. Furthermore, this review offers insights into the practical utility of OoC platforms for modeling tissue barriers, showcasing their potential and drawbacks while providing future projections for innovative technologies.

Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.

Droplet-based microfluidics: an efficient high-throughput portable system for cell encapsulation

One of the goals of tissue engineering and regenerative medicine is restoring primary living tissue function by manufacturing a 3D microenvironment. One of the main challenges is protecting implanted non-autologous cells or tissues from the host immune system. Cell encapsulation has emerged as a promising technique for this purpose. It involves entrapping cells in biocompatible and semi-permeable microcarriers made from natural or synthetic polymers that regulate the release of cellular secretions. In recent years, droplet-based microfluidic systems have emerged as powerful tools for cell encapsulation in tissue engineering and regenerative medicine. These systems offer precise control over droplet size, composition, and functionality, allowing for creating of microenvironments that closely mimic native tissue. Droplet-based microfluidic systems have extensive applications in biotechnology, medical diagnosis, and drug discovery. This review summarises the recent developments in droplet-based microfluidic systems and cell encapsulation techniques, as well as their applications, advantages, and challenges in biology and medicine. The integration of these technologies has the potential to revolutionise tissue engineering and regenerative medicine by providing a precise and controlled microenvironment for cell growth and differentiation. By overcoming the immune system's challenges and enabling the release of cellular secretions, these technologies hold great promise for the future of regenerative medicine.