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Drug-drug interactions (DDIs) are a significant source of morbidity and adverse drug events (ADEs), particularly in situations of polypharmacy and complex medication regimens. While rules-based software integrated in electronic health records (EHRs) has demonstrated proficiency in identifying DDIs present in medication regimens, large language model (LLM) based identification requires thorough benchmarking and performance evaluation using high-quality datasets for safe use. The purpose of this study was to develop a series of performance benchmarking experiments specifically for LLM performance in identification and management of DDIs using a specifically curated clinician-annotated dataset of clinically-relevant DDIs. Show less

Fluorescein isothiocyanate-conjugated Annexin V in combination with propidium iodide (PI) labelling is a widely used flow cytometric assay for quantifying apoptotic and necrotic cells in anticancer studies. However, increasing evidence suggests that double-positive cells, or the Annexin V⁺/PI⁺ fraction, may represent not only late apoptosis but also different modalities of regulated cell death, including necroptosis, pyroptosis, ferroptosis, and cuproptosis. By collating findings from preclinical studies across different cancer cells, this review highlights the need for consensus in interpreting Annexin V⁺/PI⁺ populations. In the absence of molecular and/or microscopy data, this fraction is more appropriately classified as undergoing 'late-stage cell death'. In short, establishing standardised interpretive criteria is crucial to enhance understanding, facilitate cross-study comparability, and improve the translational relevance of anticancer research. Show less

In nature, life is inherently dissipative. Cells continuously consume energy (such as ATP) to sustain homeostasis, drive metabolism, and respond dynamically to environmental cues. Inspired by this principle, we develop a synthetic protocell system that exhibits dissipative behavior and initiates metabolic-like processes. Our design features synthetic vesicles formed from a cationic surfactant, which undergo a fuel-driven transformation into coacervate protocells via liquid-liquid phase separation. Dissipation is achieved through alkaline phosphatase (ALP)-catalyzed ATP hydrolysis, driving the reverse transition from coacervates back to vesicles. The distinct physicochemical properties and internal organization of vesicle and coacervate protocells enable us to design functional regulators capable of producing secondary signals, such as fluorescence and enzymatic products. This work offers a strategy for engineering enzymatic reaction-regulated dissipative behaviors of protocell systems that emulate key aspects of cellular metabolism, representing a step toward synthetic life-like systems with dynamic behavior and functional complexity. Show less

Helicates and helicenes represent two prominent classes of synthetic molecular helices, desirable for their potential in chiroptical applications. Incorporating boron into their backbone presents a promising strategy to enhance the optical properties; however, the development of boron-doped helical systems featuring tunable emission, high configurational stability, and strong chiroptical response has been limited by synthetic challenges. We report the chemistry of bora[7]helicene and its dimeric diborahelicate. While the dimeric form is thermodynamically favored in the haloborane precursor, saturation of the boron coordination sphere by exogenous carbene or carbone ligands induces monomerization, reverting the structure to the bora[7]helicene. By employing a variety of ligands, late-stage structural diversification was achieved, yielding the first examples of cationic boron helices, which show exceptional emission tunability across the entire visible spectrum, and chiroptical responses surpassing those of previously reported [7]helicenes. Theoretical studies indicate that the double-helix geometry and the intramolecular charge transfer play a significant role in achieving high dissymmetry factors. Show less

BACKGROUND: Drug repositioning is a pivotal strategy in pharmaceutical research, offering accelerated and cost-effective therapeutic discovery. However, biomedical information relevant to drug repositioning is often complex, dispersed, and underutilized due to limitations in traditional extraction methods, such as reliance on annotated data and poor generalizability. Large language models (LLMs) show promise but face challenges such as hallucinations and interpretability issues. OBJECTIVE: This study proposed long chain-of-thought for drug repositioning knowledge extraction (LCoDR-KE), a lightweight and domain-specific framework to enhance LLMs' accuracy and adaptability in extracting structured biomedical knowledge for drug repositioning. METHODS: A domain-specific schema defined 11 entities (eg, drug, disease) and 18 relationships (eg, treats, is biomarker of). Following the established schema architecture, we constructed automatic annotation based on 10,000 PubMed abstracts via chain-of-thought prompt engineering. A total of 1000 expert-validated abstracts were curated into a drug repositioning corpus, a high-quality specialized corpus, while the remaining entries were allocated for model training purposes. Then, the proposed LCoDR-KE framework combined supervised fine-tuning of the Qwen2.5-7B-Instruct model with reinforcement learning and dual-reward mechanisms. Performance was evaluated against state-of-the-art models (eg, conditional random fields, Bidirectional Encoder Representations From Transformers, BioBERT, Qwen2.5, DeepSeek-R1, OpenBioLLM-70B, and model variants) using precision, recall, and F1-score. In addition, the convergence of the training method was assessed by analyzing performance progression across iteration steps. RESULTS: LCoDR-KE achieved an entity F1 of 81.46% (eg, drug 95.83%, disease 90.52%) and triplet F1 of 69.04%, outperforming traditional models and rivaling larger LLMs (DeepSeek-R1: entity F1=84.64%, triplet F1=69.02%). Ablation studies confirmed the contributions of supervised fine-tuning (8.61% and 20.70% F1 drop if removed) and reinforcement learning (6.09% and 14.09% F1 drop if removed). The training process demonstrated stable convergence, validated through iterative performance monitoring. Qualitative analysis of the model's chain-of-thought outputs showed that LCoDR-KE performed structured and schema-aware reasoning by validating entity types, rejecting incompatible relations, enforcing constraints, and generating compliant JSON. Error analysis revealed 4 main types of mistakes and challenges for further improvement. CONCLUSIONS: LCoDR-KE enhances LLMs' domain-specific adaptability for drug repositioning by offering an open-source drug repositioning corpus and a long chain-of-thought framework based on a lightweight LLM model. This framework supports drug discovery and knowledge reasoning while providing scalable, interpretable solutions applicable to broader biomedical knowledge extraction tasks. Show less

Drug-drug interactions (DDI) are an important cause of adverse drug reactions (ADRs). Could large language models (LLMs) serve as valuable tools for pharmacovigilance specialists in detecting DDIs that lead to ADR notifications? Show less

Introduction Mitochondria are essential organelles for many aspects of cellular homeostasis. They play an indispensable role in the development and progression of diseases, particularly cancer which is a major cause of death worldwide. We analyzed the scientific research output on mitochondria and cancer via PubMed and Web of Science over the period 1990–2023. Methods Bibliometric analysis was performed by extracting data linking mitochondria to cancer pathogenesis over the period 1990–2023 from the PubMed database which has a precise and specific search engine. Only articles and reviews were considered. Since PubMed does not support analyses by countries or institutions, we utilized InCites, an analytical tool developed and marketed by Clarivate Analytics. We also used the VOSviewer software developed by the Centre for Science and Technology Studies (Bibliometric Department of Leiden University, Leiden, Netherlands), which enables us to graphically represent links between countries, authors or keywords in cluster form. Finally, we used iCite, a tool developed by the NIH (USA) to access a dashboard of bibliometrics for papers associated with a portfolio. This module can therefore be used to measure whether the research carried out is still basic, translational or clinical. Results In total, 169,555 publications were identified in PubMed relating to ‘mitochondria’, of which 34,949 (20.61%) concerned ‘mitochondria’ and ‘dysfunction’ and 22,406 (13.21%) regarded ‘mitochondria’ and ‘cancer’. Hence, not all mitochondrial dysfunctions may lead to cancer or enhance its progression. Qualitatively, the disciplines of journals were classified into 166 categories among which cancer specialty accounts for only 4.7% of publications. Quantitatively, our analysis showed that cancer/neoplasms in the liver (2569 articles) were placed in the first position. USA occupied the first position among countries contributing the highest number of publications (5695 articles), whereas Egypt came in the thirty-eight position with 84 publications (0.46%). Importantly, USA is the first-ranked country having both the top 1% and 10% impact indicators with 207 and 1459 articles, respectively. By crossing the query ‘liver neoplasms’ (155,678) with the query ‘mitochondria’ (169,555), we identified 1336 articles in PubMed over the study period. Among these publications, research areas were classified into 65 categories with the highest percentage of documents included in biochemistry and molecular biology (28.92%), followed by oncology (23.31%). Conclusions This study underscores the crucial yet underrepresented role of mitochondria in cancer research. Despite their significance in cancer pathogenesis, the proportion of related publications remains relatively low. Our findings Supplementary Information The online version contains supplementary material available at https://​doi.​org/​10.​1007/​s12672-​025-​ 02139-5. * Abeer El Wakil, abeer_elwakil@alexu.edu.eg; Patrick Devos, patrick.devos@univ-lille.fr; Heba Abdelmegeed, hn.abdelmegeed@ nrc.sci.eg; Alaa Kamel, alaa.kamel_pg@alexu.edu.eg | 1Department of Biological and Geological Sciences, Faculty of Education, Alexandria University, Alexandria 21526, Egypt. 2Université Lille, Lillometrics, 59000 Lille, France. 3CHU Lille, Direction de la Recherche et de l’Innovation, 59000 Lille, France. 4Department of Chemistry of Natural Compounds, National Research Centre, Giza, Egypt. 5Department of Zoology, Faulty of Science, Alexandria University, Alexandria, Egypt. Discover Oncology (2025) 16:517 | https://doi.org/10.1007/s12672-025-02139-5 Vol.:(0123456789) Research Discover Oncology (2025) 16:517 | https://doi.org/10.1007/s12672-025-02139-5 highlight the need for further research to deepen our understanding of mitochondrial mechanisms in cancer, which could pave the way for new therapeutic strategies. Graphical Abstract Show less

High-Grade Serous Ovarian Cancer (HGSOC) is the most common and lethal subtype of ovarian cancer, known for its high aggressiveness and extensive genomic alterations. Typically diagnosed at an advanced stage, HGSOC presents formidable challenges in drug therapy. The limited efficacy of standard treatments, development of chemoresistance, scarcity of targeted therapies, and significant tumor heterogeneity render this disease incurable with current treatment options, highlighting the urgent need for novel therapeutic approaches to improve patient outcomes. In this study we report a straightforward and stereoselective synthetic route to novel Pd(II)-vinyl and -butadienyl complexes bearing a wide range of monodentate and bidentate ligands. Most of the synthesized complexes exhibited good to excellent in vitro anticancer activity against ovarian cancer cells. Particularly promising is the water-soluble complex bearing two PTA (1,3,5-triaza-7-phosphaadamantane) ligands and the Pd(II)-butadienyl fragment. This compound combines excellent cytotoxicity towards cancer cells with substantial inactivity towards non-cancerous ones. This derivative was selected for further studies on ex vivo tumor organoids and in vivo mouse models, which demonstrate its remarkable efficacy with surprisingly low collateral toxicity even at high dosages. Moreover, this class of compounds appears to operate through a ferroptotic mechanism, thus representing the first such example for an organopalladium compound. Show less

Computational drug discovery is essential for screening potential treatments and reducing the costs and time associated with proposing or combining drugs for disease management. Despite the extensive research conducted in this field, it remains an emerging area, particularly with the advent of machine learning, deep learning, and large language models (LLMs). This systematic review examines the integration of machine learning and deep learning techniques in drug discovery, concentrating on three critical areas: drug−drug interactions (DDIs), drug-target interactions (DTIs), and adverse drug reactions (ADRs). The review analyzes over 100 papers published between 2020 and 2025, categorizing the methods into deep learning, machine learning, graph learning, and hybrid models. It highlights the transformative impact of natural language processing (NLP) and LLMs in extracting meaningful insights from biomedical literature and chemical data. Furthermore, this work introduces key databases and data sets widely utilized in drug discovery. Additionally, this review identifies gaps in the existing research, such as the lack of comprehensive studies that simultaneously address DDI, DTI, and ADR extraction, and it proposes a more holistic approach to fill these gaps. The paper concludes by thoroughly evaluating various models, underscoring their performance metrics. Show less

Background/Objectives: Cell viability assays play a crucial role in cancer research and the development of effective treatments. Evaluating the efficacy of conventional treatments across different tumor profiles is essential for understanding patient resistance to chemotherapy and relapse. The IC50 index has been commonly used as a guide in these assays. The idea behind the IC50 index is to compare cell proliferation under treatment with respect to a control population exposed to the same treatment. The index requires normalization to a control and is time dependent. These aspects are disadvantages, as small variations yield different results. In this article, we propose a new method to analyze cell viability assays. Methods: This method involves calculating the effective growth rate for both control (untreated) cells and cells exposed to a range of drug doses for short times, during which exponential proliferation can be assumed. The concentration dependence of the effective growth rate gives a real estimate of the treatment on cell proliferation. A curve fit of the effective growth rate related to concentration yields the concentration corresponding to a given effective growth rate. Results: We use this estimation to calculate the IC50 index and introduce two new parameters (ICr0 and ICrmed) to compare treatment efficacy under different culture conditions or cell lines. Conclusions: In summary, this study presents a new method to analyze cell viability assays and introduces two more precise parameters, improving the comparison and evaluation of efficacy under different conditions. Show less

Received: 25 June 2025 Revised: 8 August 2025 Accepted: 13 August 2025 Published: 14 August 2025 Citation: Jin, Z.; Zhang, Q.; Pan, Y.; Chen, H.; Zhou, K.; Cai, H.; Huang, P. Roles and Prospective Applications of Ferroptosis Suppressor Protein 1 (FSP1) in Malignant Tumor Treatment. Curr. Oncol. 2025, 32, 456. https:// doi.org/10.3390/curroncol32080456 Show less

An analysis of dose, dose frequency, human pharmacokinetics, and potential drug-drug interactions (DDI) was performed on small-molecule oral drugs approved by the FDA from 2020 to 2024 (n = 104). Although most oral drugs are administered QD (67%), BID and TID regimens are also regularly approved (32%). First-in-class (FIC) drugs and drugs with Orphan Drug Designation (ODD) have a higher frequency of BID or TID administration compared to drugs without those designations (BID and TID = 50% for FIC drugs vs 19% for non-FIC; BID and TID = 41% for ODD vs 20% non-ODD). Most drugs are >95% plasma protein bound (58%), with a large fraction >99% bound (29%). Of these drugs, 22% have black box warnings and 42% list contraindications. An examination of DDI revealed the most frequent warning around CYP3A4 induction (60%). These findings will help medicinal chemists better understand and predict typical and nontypical profiles of oral drugs. Show less

Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-making often relies on medicinal chemistry intuition. The present study explores the application of collective intelligence in drug discovery, focusing on lead optimization. Ninety-two Sanofi researchers with diverse expertise participated anonymously in an exercise centered on ADMET-related questions. Their feedback was used to build a collective intelligence agent, which was compared to an artificial intelligence model. The study led to three major conclusions: first, collective intelligence improves decision-making in optimizing ADMET endpoints, compared to individual decisions. Second, collective intelligence outperforms artificial intelligence for all other endpoints but hERG inhibition. Finally, we observe complementarity between collective human and artificial intelligence. Overall, this research highlights the potential of collective intelligence in drug discovery and the importance of a synergistic approach combining human and artificial intelligence in project decision making. Show less

Background/ObjectivesNew computational methods, based on statistical, machine learning, and deep learning techniques using drug-related entities (e.g., genes, protein bindings, etc.), help reduce the costs of in-vitro experiments through drug-drug interaction prediction (DDIp). This review examines recent advances in DDIp. It presents an in-depth review of the state-of-the-art studies relating to semi-supervised, supervised, self-supervised learning, and other techniques such as graph-based learning and matrix factorization methods for predicting DDIs. All possible interactions between drugs are not known, and accurately predicting interactions is even more difficult due to the complex nature of drug-drug interactions (DDI).MethodsOf the 49 papers published in Web of Science in the last 6 years, 24 papers were considered relevant based on information presented in their titles and abstracts. The included articles focus specifically on predicting DDIs using a type of machine learning algorithm. Excluded articles focused on drug discovery, drug repurposing, molecular representation, or the extraction of biomedical interactions. The methodology, results limitations, and future research directions were studied for each paper. Common challenges, limitations, and future research directions were analyzed.Results and conclusionThe main limitations are class imbalance, poor performance on new drugs, limited explainability, and the need for additional data sources. Show less

Ferroptosis, an iron-dependent regulated cell death, is implicated in several diseases, including cancer and neurodegeneration. While most ferroptosis inhibitors act as radical-trapping antioxidants, direct modulation of pro-ferroptotic enzymes remains underexplored. Acyl-coenzyme A synthetase long-chain family member 4 (ACSL4), a key regulator of ferroptosis, has emerged as a promising therapeutic target. Here, we report a fragment-based screening that identified a benzofuran hit (compound 8, IC₅₀ = 33 μM), leading to the discovery of two selective ACSL4 inhibitors: compound 15b (LIBX-A402, IC₅₀ = 0.33 μM) and compound 21 (LIBX-A403, IC₅₀ = 0.049 μM). Compound 21 is the most potent ACSL4 inhibitor reported to date and shows no activity against ACSL3. Molecular modeling and mutagenesis support its binding in the ACSL4 fatty acid pocket. The strong antiferroptotic activity of both compounds in cells, together with confirmed target engagement for 21, underscores the relevance of ACSL4 as a target for ferroptosis modulation. Show less

Photocatalytic cancer therapy (PCT) has emerged as a cutting-edge anticancer mechanism of action, harnessing light energy to mediate the catalytic oxidation of intracellular substrates. PCT is of significant current importance due to its potential to address the limitations of conventional chemotherapy, particularly drug resistance and side effects. This approach offers a noninvasive, targeted cancer treatment option by utilizing metal-based photocatalysts to induce redox and metabolic disorders within cancer cells. The photocatalysts disrupt the cancer cell metabolism by converting NADH/NAD(P)H to NAD⁺/NAD(P)⁺ via catalytic photoredox processes, altering intracellular NAD⁺/NADH or NAD(P)⁺/NAD(P)H ratios, which are crucial for cellular metabolism. Ir(III), Ru(II), Re(I), and Os(II) photocatalysts demonstrated promising PCT efficacy. Despite these developments, gaps remain in the literature for translating this new anticancer mechanism into clinical trials. This Perspective critically examines the developments in this research area and provides future directions for designing efficient photocatalysts for PCT. Show less

Background: Adverse drug reactions (ADRs) are harmful side effects of medications. Social media provides real-time, patient-generated data, though its unstructured format presents challenges. Natural language processing and transfer learning offer promising solutions. Objective: This study aimed to evaluate whether transformer-based models fine-tuned on a general ADR dataset can effectively classify ADRs from tweets related to glucagon-like peptide-1 (GLP-1) receptor agonists and to benchmark their performance against state-ofthe-art large language models (LLMs). Show less

Water is arguably one of the most important chemicals essential for the functioning of biological molecules. In the context of DNA, it plays a crucial role in stabilizing and modulating its structure and function. The discovery of water-bound motifs in crystal structures has greatly improved our understanding of the interactions between structured water molecules and DNA. In this manuscript, we review the role of water in mediating biologically relevant DNA structures, in particular those arising from epigenetic modifications and higher-order structures such as G-quadruplexes and i-motifs. We also examine water-mediated interactions between DNA and various small molecules, including groove binders and intercalators, and emphasize their importance for DNA function and therapeutic development. Finally, we discuss recent advances in tools and techniques for predicting water interactions in nucleic acid structures. By offering a fresh perspective on the role of water, this review underscores its importance as a molecular modulator of DNA structure and function. Show less

Structure-based drug design is rapidly evolving, driven by advances in both physics-based and knowledge-based methods. These computational approaches are increasingly integrated across all stages of drug discovery. Despite remarkable progress, challenges remain in achieving accuracy, generalizability, computational efficiency, and chemical synthesizability. In this review, we provide a critical overview of advances, strengths, and limitations of recent methods. We also discuss synergies between the two concepts that hold promises for future advancements towards their practical applicability. Show less

Mitochondria are the energy production centers in cells and have unique genetic information. Due to the irreplaceable function of mitochondria, mitochondrial dysfunction often leads to pathological changes. Mitochondrial dysfunction induces an imbalance between oxidation and antioxidation, mitochondrial DNA (mtDNA) damage, mitochondrial dynamics dysregulation, and changes in mitophagy. It results in oxidative stress due to excessive reactive oxygen species (ROS) generation, which contributes to cell damage and death. Mitochondrial dysfunction can also trigger inflammation through the activation of damage-associated molecular patterns (DAMPs), inflammasomes and inflammatory cells. Besides, mitochondrial alterations in the functional regulation, energy metabolism and genetic stability accompany the aging process, and there has been a lot of evidence suggesting that oxidative stress and inflammation, both of which are associated with mitochondrial dysfunction, are predisposing factors of aging. Therefore, this review hypothesizes that mitochondria serve as central hubs regulating oxidative stress, inflammation, and aging, and their dysfunction contributes to various diseases, including cancers, cardiovascular diseases, neurodegenerative disorders, metabolic diseases, sepsis, ocular pathologies, liver diseases, and autoimmune conditions. Moreover, we outline therapies aimed at various mitochondrial dysfunctions, highlighting their performance in animal models and human trials. Additionally, we focus on the limitations of mitochondrial therapy in clinical applications, and discuss potential future research directions for mitochondrial therapy. Show less

Abstract Ferroptosis, a novel form of regulated cell death induced by the excessive accumulation of lipid peroxidation products, plays a pivotal role in the suppression of tumorigenesis. Two prominent mitochondrial ferroptosis defense systems are glutathione peroxidase 4 (GPX4) and dihydroorotate dehydrogenase (DHODH), both of which are localized within the mitochondria. However, the existence of supplementary cellular defense mechanisms against mitochondrial ferroptosis remains unclear. Our findings unequivocally demonstrate that inactivation of mitochondrial respiratory chain complex I (MCI) induces lipid peroxidation and consequently invokes ferroptosis across GPX4 low-expression cancer cells. However, in GPX4 high expression cancer cells, the MCI inhibitor did not induce ferroptosis, but increased cell sensitivity to ferroptosis induced by the GPX4 inhibitor. Overexpression of the MCI alternative protein yeast NADH-ubiquinone reductase (NDI1) not only quells ferroptosis induced by MCI inhibitors but also confers cellular protection against ferroptosis inducers. Mechanically, MCI inhibitors actuate an elevation in the NADH level while concomitantly diminishing the CoQH2 level. The manifestation of MCI inhibitor-induced ferroptosis can be reversed by supplementation with mitochondrial-specific analogues of CoQH2. Notably, MCI operates in parallel with mitochondrial-localized GPX4 and DHODH to inhibit mitochondrial ferroptosis, but independently of cytosolically localized GPX4 or ferroptosis suppressor protein 1(FSP1). The MCI inhibitor IACS-010759, is endowed with the ability to induce ferroptosis while concurrently impeding tumor proliferation in vivo. Our results identified a ferroptosis defense mechanism mediated by MCI within the mitochondria and suggested a therapeutic strategy for targeting ferroptosis in cancer treatment. Show less

The use of multiple medications increases the risk of harmful drug-drug interactions (DDIs). Conventional DDI screening databases vary in coverage and often trigger low-relevance alerts, contributing to alert fatigue. Large language models (LLMs) have emerged as potential tools for DDI identification, however, their performance compared to established databases using real-world patient data remains under-explored. Show less

Researchers are using new molecules, engineered immune cells and gene therapy to kill senescent cells and treat age-related diseases. Researchers are using new molecules, engineered immune cells and gene therapy to kill senescent cells and treat age-related diseases. Show less

Programmed cell death (PCD) is a fundamental biological process for maintaining cellular equilibrium and regulating development, health, and disease across all living organisms. Among the various types of PCD, apoptosis plays a pivotal role in numerous diseases, notably cancer. Cancer cells frequently develop mechanisms to evade apoptosis, increasing resistance to standard chemotherapy treatments. This resistance has prompted extensive research into alternative mechanisms of programmed cell death. One such pathway is oncosis, characterized by significant energy consumption, cell swelling, dilation of the endoplasmic reticulum, mitochondrial swelling, and nuclear chromatin aggregation. Recent research suggests that oncosis can impact conditions such as chemotherapeutic cardiotoxicity, myocardial ischemic injury, stroke, and cancer, mediated by specific oncosis-related proteins. In this review, we provide a detailed examination of the morphological and molecular features of oncosis and discuss various natural or small molecule compounds that can induce this type of cell death. Additionally, we summarize the current understanding of the molecular mechanisms underlying oncosis and its role in both normal physiology and pathological conditions. These insights aim to illuminate future research directions and propose innovative strategies for leveraging oncosis as a therapeutic tool against human diseases and cancer resistance. Show less

Motivation: Drug–target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could Show less

Cisplatin (cDDP) resistance is a matter of concern in triple-negative breast cancer therapeutics. We measured the metabolic response of cDDP-sensitive (S) and -resistant (R) MDAMB-231 cells to Pd2Spermine(Spm) (a possible alternative to cDDP) compared to cDDP to investigate (i) intrinsic response/ resistance mechanisms and (ii) the potential cytotoxic role of Pd2Spm. Cell extracts were analyzed by untargeted nuclear magnetic resonance metabolomics, and cell media were analyzed for particular metabolites. CDDP-exposed S cells experienced enhanced antioxidant protection and small deviations in the tricarboxylic acid cycle (TCA), pyrimidine metabolism, and lipid oxidation (proposed cytotoxicity signature). R cells responded more strongly to cDDP, suggesting a resistance signature of activated TCA cycle, altered AMP/ADP/ATP and adenine/uracil fingerprints, and phospholipid biosynthesis (without significant antioxidant protection). Pd2Spm impacted more markedly on R/S cell metabolisms, inducing similarities to cDDP/S cells (probably reflecting high cytotoxicity) and strong additional effects indicative of amino acid depletion, membrane degradation, energy/ nucleotide adaptations, and a possible beneficial intracellular γ-aminobutyrate/glutathione-mediated antioxidant mechanism. ■ Show less

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. Show less

Background: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. ARTICLE HISTORY Objective: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. Received: June 07, 2024 Revised: August 11, 2024 Accepted: August 19, 2024 Methods: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literaDOI: 10.2174/0113894501330963240905083020 ture. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. Results: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. Conclusion: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process. Show less