Automating drug discovery

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.

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.

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.

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.

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.

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.

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.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Finding the most potent compounds using active learning on molecular pairs

Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited and model exploitation might lead to analog identification with limited scaffold diversity. Here, we present ActiveDelta, an adaptive approach that leverages paired molecular representations to predict improvements from the current best training compound to prioritize further data acquisition. We apply the ActiveDelta concept to both graph-based deep (Chemprop) and tree-based (XGBoost) models during exploitative active learning for 99 Ki benchmarking datasets. We show that both ActiveDelta implementations excel at identifying more potent inhibitors compared to the standard exploitative active learning implementations of Chemprop, XGBoost, and Random Forest. The ActiveDelta approach is also able to identify more chemically diverse inhibitors in terms of their Murcko scaffolds. Finally, deep models such as Chemprop trained on data selected through ActiveDelta approaches can more accurately identify inhibitors in test data created through simulated time-splits. Overall, this study highlights the large potential for molecular pairing approaches to further improve popular active learning strategies in low data regimes by enabling faster and more accurate identification of more diverse molecular hits against critical drug targets.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Current Treatments, Emerging Therapeutics, and Natural Remedies for Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic, lifelong disorder characterized by inflammation of the gastrointestinal (GI) tract. The exact etiology of IBD remains incompletely understood due to its multifaceted nature, which includes genetic predisposition, environmental factors, and host immune response dysfunction. Currently, there is no cure for IBD. This review discusses the available treatment options and the challenges they present. Importantly, we examine emerging therapeutics, such as biologics and immunomodulators, that offer targeted treatment strategies for IBD. While many IBD patients do not respond adequately to most biologics, recent clinical trials combining biologics with small-molecule drugs (SMDs) have provided new insights into improving the IBD treatment landscape. Furthermore, numerous novel and specific therapeutic targets have been identified. The high cost of IBD drugs poses a significant barrier to treatment, but this challenge may be alleviated with the development of more affordable biosimilars. Additionally, emerging point-of-care protein biomarkers from serum and plasma are showing potential for enhancing the precision of IBD diagnosis and prognosis. Several natural products (NPs), including crude extracts, small molecules, and peptides, have demonstrated promising anti-inflammatory activity in high-throughput screening (HTS) systems and advanced artificial intelligence (AI)-assisted platforms, such as molecular docking and ADMET prediction. These platforms are advancing the search for alternative IBD therapies derived from natural sources, potentially leading to more affordable and safer treatment options with fewer side effects.

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines

With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy cancer cells. However, enhancing the efficacy of cancer vaccines remains a challenge. Adjuvants, which enhance the immune response to antigens and improve vaccine effectiveness, have faced limitations in recent years, resulting in few novel adjuvants being identified. The advancement of artificial intelligence (AI) technology in drug development has provided a foundation for adjuvant screening and application, leading to a diversification of adjuvants. This article reviews the significant role of tumor vaccines in basic research and clinical treatment and explores the use of AI technology to screen novel adjuvants from databases. The findings of this review offer valuable insights for the development of new adjuvants for next-generation vaccines.

Metis: a python-based user interface to collect expert feedback for generative chemistry models

One challenge that current de novo drug design models face is a disparity between the user's expectations and the actual output of the model in practical applications. Tailoring models to better align with chemists' implicit knowledge, expectation and preferences is key to overcoming this obstacle effectively. While interest in preference-based and human-in-the-loop machine learning in chemistry is continuously increasing, no tool currently exists that enables the collection of standardized and chemistry-specific feedback. Metis is a Python-based open-source graphical user interface (GUI), designed to solve this and enable the collection of chemists' detailed feedback on molecular structures. The GUI enables chemists to explore and evaluate molecules, offering a user-friendly interface for annotating preferences and specifying desired or undesired structural features. By providing chemists the opportunity to give detailed feedback, allows researchers to capture more efficiently the chemist's implicit knowledge and preferences. This knowledge is crucial to align the chemist's idea with the de novo design agents. The GUI aims to enhance this collaboration between the human and the "machine" by providing an intuitive platform where chemists can interactively provide feedback on molecular structures, aiding in preference learning and refining de novo design strategies. Metis integrates with the existing de novo framework REINVENT, creating a closed-loop system where human expertise can continuously inform and refine the generative models.Scientific contributionWe introduce a novel Graphical User Interface, that allows chemists/researchers to give detailed feedback on substructures and properties of small molecules. This tool can be used to learn the preferences of chemists in order to align de novo drug design models with the chemist's ideas. The GUI can be customized to fit different needs and projects and enables direct integration into de novo REINVENT runs. We believe that Metis can facilitate the discussion and development of novel ways to integrate human feedback that goes beyond binary decisions of liking or disliking a molecule.

Late-Stage C(sp2)-C(sp3) Diversification via Nickel Oxidative Addition Complexes

Herein, we describe nickel oxidative addition complexes (Ni-OACs) of drug-like molecules as a platform to rapidly generate lead candidates with enhanced C(sp3) fraction. The potential of Ni-OACs to access new chemical space has been assessed not only in C(sp2)-C(sp3) couplings but also in additional bond formations without recourse to specialized ligands and with improved generality when compared to Ni-catalyzed reactions. The development of an automated diversification process further illustrates the robustness of Ni-OACs, thus offering a new gateway to expedite the design-make-test-analyze (DMTA) cycle in drug discovery.

Simultaneous reaction- and analytical model building using dynamic flow experiments to accelerate process development

In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies encompass prolonged timelines for the development of both a reaction model and analytical models. The development of both methods are often separated into different departments and can require an iterative optimization process. Addressing this issue, we introduce an innovative dual modeling approach, combining the development of a Process Analytical Technology (PAT) strategy with reaction optimization. This integrated approach is exemplified in diverse amidation reactions and the synthesis of the API benznidazole. The platform, characterized by a high degree of automation and minimal operator involvement, achieves PAT calibration through a "standard addition" approach. Dynamic experiments are executed to screen a broad process space and gather data for fitting kinetic parameters. Employing an open-source software program facilitates rapid kinetic parameter fitting and additional in silico optimization within minutes. This highly automated workflow not only expedites the understanding and optimization of chemical processes, but also holds significant promise for time and resource savings within the pharmaceutical industry.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

A droplet robotic system enabled by electret-induced polarization on droplet

Robotics for scientific research are evolving from grasping macro-scale solid materials to directly actuating micro-scale liquid samples. However, current liquid actuation mechanisms often restrict operable liquid types or compromise the activity of biochemical samples by introducing interfering mediums. Here, we propose a robotic liquid handling system enabled by a novel droplet actuation mechanism, termed electret-induced polarization on droplet (EPD). EPD enables all-liquid actuation in principle and experimentally exhibits generality for actuating various inorganic/organic liquids with relative permittivity ranging from 2.25 to 84.2 and volume from 500 nL to 1 mL. Moreover, EPD is capable of actuating various biochemical samples without compromising their activities, including various body fluids, living cells, and proteins. A robotic system is also coupled with the EPD mechanism to enable full automation. EPD's high adaptability with liquid types and biochemical samples thus promotes the automation of liquid-based scientific experiments across multiple disciplines.

QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design

Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.

Aptamer-based assembly systems for SARS-CoV-2 detection and therapeutics

Nucleic acid aptamers are oligonucleotide chains with molecular recognition properties. Compared with antibodies, aptamers show advantages given that they are readily produced via chemical synthesis and elicit minimal immunogenicity in biomedicine applications. Notably, aptamer-encoded nucleic acid assemblies further improve the binding affinity of aptamers with the targets due to their multivalent synergistic interactions. Specially, aptamers can be engineered with special topological arrangements in nucleic acid assemblies, which demonstrate spatial and valence matching towards antigens on viruses, thus showing potential in the detection and therapeutic applications of viruses. This review presents the recent progress on the aptamers explored for SARS-CoV-2 detection and infection treatment, wherein applications of aptamer-based assembly systems are introduced in detail. Screening methods and chemical modification strategies for aptamers are comprehensively summarized, and the types of aptamers employed against different target domains of SARS-CoV-2 are illustrated. The evolution of aptamer-based assembly systems for the detection and neutralization of SARS-CoV-2, as well as the construction principle and characteristics of aptamer-based DNA assemblies are demonstrated. The typically representative works are presented to demonstrate how to assemble aptamers rationally and elaborately for specific applications in SARS-CoV-2 diagnosis and neutralization. Finally, we provide deep insights into the current challenges and future perspectives towards aptamer-based nucleic acid assemblies for virus detection and neutralization in nanomedicine.

Bridging the gap between target-based and phenotypic-based drug discovery

INTRODUCTION

The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.

AREAS COVERED

This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.

EXPERT OPINION

The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

An Automated Robotic Interface for Assays: Facilitating Machine Learning in Drug Discovery by the Automation of Physicochemical Property Assays

Measuring the physicochemical properties of molecules is an iterative but integral process in the drug development process. A strategy to overcome the challenges in maximizing assay throughput relies on the usage of in silico machine learning (ML) prediction models trained on experimental data. Consequently, the performance of these in silico models are dependent on the quality of the utilized experimental data. To improve the data quality, we have designed and implemented an automated robotic system to prepare and run physicochemical property assays (Automated Robotic Interface for Assays, ARIA) with an increase in sample throughput of 6 to10-fold. Through this process, we overcame major challenges and achieved consistent reproducible assay data compared to semiautomated assay preparation.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

A decentralized solid compound storage facility managed by a centralized electronic platform at a growing drug discovery company

Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.

The present state and challenges of active learning in drug discovery

Active learning (AL) is an iterative feedback process that efficiently identifies valuable data within vast chemical space, even with limited labeled data. This characteristic renders it a valuable approach to tackle the ongoing challenges faced in drug discovery, such as the ever-expanding explore space and the limitations of labeled data. Consequently, AL is increasingly gaining prominence in the field of drug development. In this paper, we comprehensively review the application of AL at all stages of drug discovery, including compounds-target interaction prediction, virtual screening, molecular generation and optimization, as well as molecular properties prediction. Additionally, we discuss the challenges and prospects associated with the current applications of AL in drug discovery.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low

Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model-autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART-another deep learning generative model, but exploiting similar principles of navigation in a chemical space.

Emerging opportunities of using large language models for translation between drug molecules and indications

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

A Semi-Automated, High-Throughput Approach for the Synthesis and Identification of Highly Photo-Cytotoxic Iridium Complexes

The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise-and-test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well-tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure-activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi-automated synthesise-and-test protocol to obtain multiplex data within 72 hours.

Navigating the future: Microfluidics charting new routes in drug delivery

Microfluidics has emerged as a transformative force in the field of drug delivery, offering innovative avenues to produce a diverse range of nano drug delivery systems. Thanks to its precise manipulation of small fluid volumes and its exceptional command over the physicochemical characteristics of nanoparticles, this technology is notably able to enhance the pharmacokinetics of drugs. It has initiated a revolutionary phase in the domain of drug delivery, presenting a multitude of compelling advantages when it comes to developing nanocarriers tailored for the delivery of poorly soluble medications. These advantages represent a substantial departure from conventional drug delivery methodologies, marking a paradigm shift in pharmaceutical research and development. Furthermore, microfluidic platformsmay be strategically devised to facilitate targeted drug delivery with the objective of enhancing the localized bioavailability of pharmaceutical substances. In this paper, we have comprehensively investigated a range of significant microfluidic techniques used in the production of nanoscale drug delivery systems. This comprehensive review can serve as a valuable reference and offer insightful guidance for the development and optimization of numerous microfluidics-fabricated nanocarriers.

Anticancer potential of novel symmetrical and asymmetrical dihydropyridines against breast cancer via EGFR inhibition: molecular design, synthesis, analysis and screening

A series of novel symmetrical and asymmetrical dihydropyridines (HD 1-15) were designed, subjected to in silico ADMET prediction, synthesized, analyzed by IR, NMR, Mass analytical techniques and evaluated against epidermal growth factor receptor (EGFR) as inhibitors against Breast cancer. The results of predicted ADMET studies demonstrated the drug-likeness properties of the reported compounds. The in vitro cytotoxicity assessment of the synthesized compounds revealed that all of them showed good activity (IC50 ranging from 16.75 to 66.54 μM) towards MCF-7 breast cancer cells compared to the standard drug, Lapatinib (IC50 = 2.02 μM). Among these, compounds HD-6, HD-7, and HD-8 displayed the most potent activity with IC50 value of 21.26, 16.75, and 18.33 μM, respectively. Cytotoxicity of all compounds was tested on normal vero cells for comparison at different concentrations using the MTT assay. In addition to the MTT assay, the potent dihydropyridines derivatives were screened for EGFRwt kinase inhibition assay at concentrations ranging from 1 nM to 360 nM. Among the three compounds tested, HD-8 showed reasonably good inhibition with an IC50 value of 15.90 ± 1.20 nM compared to a standard Lapatinib IC50 value of 10.28 ± 1.01 nM. Based on the molecular docking study against EGFR, the most active derivatives HD-7 and HD-8 were docked against the active site of the protein and showed better binding affinity than the standard lapatinib. Additionally, molecular dynamics (MD) simulations were performed to explore the stability of the protein-ligand complex, its dynamic behavior, and the binding affinity.

Development and evaluation of 2,4-disubstituted-5-aryl pyrimidine derivatives as antibacterial agents

Designing novel candidates as potential antibacterial scaffolds has become crucial due to the lack of new antibiotics entering the market and the persistent rise in multidrug resistance. Here, we describe a new class of potent antibacterial agents based on a 5-aryl-N2,N4-dibutylpyrimidine-2,4-diamine scaffold. Structural optimization focused on the 5-aryl moiety and the bioisosteric replacement of the side chain linker atom. Screening of the synthesized compounds focused on a panel of bacterial strains, including gram-positive Staphylococcus aureus strains (Newman MSSA, methicillin- and vancomycin-resistant), and the gram-negative Escherichia coli (ΔAcrB strain). Several compounds showed broad-spectrum antibacterial activity with compound 12, bearing a 4-chlorophenyl substituent, being the most potent among this series of compounds. This frontrunner compound revealed a minimum inhibitory concentration (MIC) value of 1 µg/mL against the S. aureus strain (Mu50 methicillin-resistant S. aureus/vancomycin-intermediate S. aureus) and an MIC of 2 µg/mL against other tested strains. The most potent derivatives were further tested against a wider panel of bacteria and evaluated for their cytotoxicity, revealing further potent activities toward Streptococcus pneumoniae, Enterococcus faecium, and Enterococcus faecalis. To explore the mode of action, compound 12 was tested in a macromolecule inhibition assay. The obtained data were supported by the safety profile of compound 12, which possessed an IC50 of 12.3 µg/mL against HepG2 cells. The current results hold good potential for a new class of extended-spectrum antibacterial agents.

Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists

INTRODUCTION

The integration of artificial intelligence (AI) into clinical pharmacology could be a potential approach for accelerating drug discovery and development, improving patient care, and streamlining medical research processes.

AREAS COVERED

We reviewed the current state of AI applications in clinical pharmacology, focusing on drug discovery and development, precision medicine, pharmacovigilance, and other ventures. Key AI applications in clinical pharmacology are examined, including machine learning, natural language processing, deep learning, and reinforcement learning etc. Additionally, the evolving role of clinical pharmacologists, ethical considerations, and challenges in implementing AI in clinical pharmacology are discussed.

EXPERT OPINION

The AI could be instrumental in accelerating drug discovery, predicting drug safety and efficacy, and optimizing clinical trial designs. It can play a vital role in precision medicine by helping in personalized drug dosing, treatment selection, and predicting drug response based on genetic, clinical, and environmental factors. The role of AI in pharmacovigilance, such as signal detection and adverse event prediction, is also promising. The collaboration between clinical pharmacologists and AI experts also poses certain ethical and practical challenges. Clinical pharmacologists can be instrumental in shaping the future of AI-driven clinical pharmacology and contribute to the improvement of healthcare systems.

De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations

De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform

Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; the design-build-test-learn (DBTL) method could achieve the optimal composition from only a handful of ingredients. A vast design space needs to be explored to discover the possible global optimal composition for on-demand materials synthesis. This research developed a hypothesis-guided DBTL (H-DBTL) method combined with robots to expand the dimensions of the search space, thereby achieving a better global optimal performance. First, this study engineered the search space with knowledge-aware chemical descriptors and customized multiobjective functions to fulfill on-demand research objectives. To verify this concept, this novel method was used to optimize colorimetric ammonia sensors across a vast design space of as high as 19 variables, achieving two remarkable optimization goals within 1 week: first, a sensing array was developed for ammonia quantification of a wide dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art detection limit of 50 ppb was reached. This work demonstrates that the H-DBTL approach, combined with a robot, develops a novel paradigm for the on-demand optimization of functional materials.

Protracted molecular dynamics and secondary structure introspection to identify dual-target inhibitors of Nipah virus exerting approved small molecules repurposing

Nipah virus (NiV), with its significantly higher mortality rate compared to COVID-19, presents a looming threat as a potential next pandemic, particularly if constant mutations of NiV increase its transmissibility and transmission. Considering the importance of preventing the facilitation of the virus entry into host cells averting the process of assembly forming the viral envelope, and encapsulating the nucleocapsid, it is crucial to take the Nipah attachment glycoprotein-human ephrin-B2 and matrix protein as dual targets. Repurposing approved small molecules in drug development is a strategic choice, as it leverages molecules with known safety profiles, accelerating the path to finding effective treatments against NiV. The approved small molecules from DrugBank were used for repurposing and were subjected to extra precision docking followed by absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling. The 4 best molecules were selected for 500 ns molecular dynamics (MD) simulation followed by Molecular mechanics with generalized Born and surface area solvation (MM-GBSA). Further, the free energy landscape, the principal component analysis followed by the defined secondary structure of proteins analysis were introspected. The inclusive analysis proposed that Iotrolan (DB09487) and Iodixanol (DB01249) are effective dual inhibitors, while Rutin (DB01698) and Lactitol (DB12942) were found to actively target the matrix protein only.