Artificial Intelligence in the Life Sciences

BackgroundAdvances in medicine depend on analyzing large and complex data sources, but discovery is partly constrained by the limited time and domain expertise of human researchers. Agentic artificial intelligence (agentic AI) can accelerate discovery by automating components of the scientific workflow, including information retrieval, data analysis, and knowledge synthesis. AimOpenScientist, an open-source agentic AI co-scientist, aims to accelerate biomedical discovery by semi-autonomously investigating scientist-defined queries and generating clinically relevant, verifiable scientific insights. MethodsDomain experts evaluated OpenScientist for novel discoveries in four clinical case studies: (1) a prespecified analysis in a community-based Alzheimers disease biomarker cohort, (2) unsupervised modeling for plasma proteomic survival prediction, (3) hypothesis investigation in single-cell transcriptomic data from neurons with neurofibrillary tangles, and (4) hypothesis generation with validation in a multiple myeloma dataset with a randomized negative control. ResultsOpenScientist completed analyses in minutes that otherwise would take weeks to months of human time and expertise. It identified %ptau217 as the best predictor of amyloid PET status, generated a plasma proteomic survival model with performance comparable to published models, proposed a mechanism linking tau pathology to altered lysosomal acidification, and generated multiple myeloma hypotheses that were validated in an external cohort while distinguishing true signal from randomized controls. ConclusionOpenScientist demonstrates that open, auditable, agentic AI can support real-world clinical research by generating hypotheses, executing analyses, and discovering insights from complex datasets.

Computational screening is increasingly becoming a crucial aspect of Antibody-Drug Conjugate (ADC) research, allowing the elimination of dead ends at earlier stages and concentrating on potential candidates, which can significantly reduce the cost of development. The current state-of-the-art deep learning model, ADCNet, usually considers antibodies, antigens, linkers, and payloads as distinct features. However, this overlooks the complex context of antibody-antigen binding, which is primarily responsible for the targeting and uptake of ADCs. To address this limitation, we present ABFormer, a transformer-based framework tailored for ADC activity prediction and in-silico triage. ABFormer integrates high-resolution antibody-antigen interface information through a pretrained interaction encoder and combines it with chemically enriched linker and payload representations obtained from a fine-tuned molecular encoder. This multi-modal design replaces naive feature concatenation with biologically informed contextual embeddings that more accurately reflect molecular recognition. ABFormer outperforms in leave-pair-out evaluation and achieves 100% accuracy on a separate test set of 22 novel ADCs, while the baselines are severely mis-calibrated. Ablation study confirms that the predictive capability is predominantly driven by interaction-aware antibody-antigen representations, while small-molecule encoders enhance specificity by reducing false positives. In conclusion, ABFormer provides a reliable and efficient platform for early filtering of ADC activity and selection of candidates.

Computational prediction of blood-brain barrier (BBB) permeability has emerged as a vital alternative to traditional experimental assays, which are often resource-intensive and lowthroughput to meet the demands of early-stage drug discovery. While early machine learning approaches have shown promise, integration of traditional chemical descriptors with deep learning embeddings remains an underexplored frontier. In this paper, we introduce TITAN-BBB, a multi-modal deep-learning architecture that utilizes tabular, image, and text-based features and combines them using attention mechanisms. To evaluate, we aggregated multiple literature sources to create the largest BBB permeability dataset to date, enabling robust training for both classification and regression tasks. Our results demonstrate that TITAN-BBB achieves 86.5% of balanced accuracy on classification tasks and 0.436 of mean absolute error for regression, outperforming the state-of-the-art by 3.1 percentage points in balanced accuracy and reducing the regression error by 20%. Our approach also outperforms state-of-the-art models in both classification and regression performance, demonstrating the benefits of combining deep and domain-specific representations. The source code is publicly available at https://github.com/pcdslab/TITAN-BBB. The inference-ready model is hosted on Hugging Face at https://huggingface.co/SaeedLab/TITAN-BBB, and the aggregated BBB permeability datasets are available at https://huggingface.co/datasets/SaeedLab/BBBP.

Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages.

Long COVID affects millions of people worldwide, yet no disease-modifying treatment has been approved, and existing interventions have shown only modest and inconsistent benefits. A key reason for this limited progress is that current computational drug repurposing pipelines do not match well with the clinical reality of Long COVID. These patients often have persistent, multisystemic symptoms and may already be taking multiple medications, making treatment safety a primary concern. However, most repurposing workflows still treat safety as a downstream filter and rely on disease-associated targets rather than causal drivers. They also assume that the findings of one analysis would generalize across the diverse presentations of Long COVID. We introduce SPLIT, a safety-first repurposing framework that addresses these limitations. SPLIT prioritizes safety at the start of the candidate evaluation, integrates complementary causal inference strategies to identify likely driver genes, and uses a counterfactual substitution design to compare drugs within specific cohort contexts. When applied to cognitive and respiratory Long COVID cohorts, SPLIT revealed three main findings. First, drugs with similar predicted efficacy could have very different predicted safety profiles. Second, the drugs flagged as unfavorable were often different between the two cohorts, showing that drug prioritization is phenotype-specific. Third, SPLIT flagged 18 drugs currently under active investigation in Long COVID trials as having unfavorable predicted profiles. SPLIT provides a practical framework to identify safer, more context-appropriate candidates earlier in the process, supporting more targeted and better-tolerated treatment strategies for Long COVID.

Generative artificial intelligence (genAI) tools are increasingly used by prospective higher education (HE) applicants seeking guidance on university and programme selection. Despite rapidly expanding use, little is known about how genAI systems may introduce or amplify bias in undergraduate admissions decision-making. Here, we systematically examined patterns of bias across three widely used genAI chatbots (ChatGPT, Copilot, Gemini) using neuroscience as a representative UK undergraduate programme. We constructed 216 prompts that varied by applicant characteristics (e.g. gender, study type, academic attainment). Each prompt was submitted to all three chatbots, generating 648 responses and 3240 individual programme recommendations. Output responses underwent text analysis (e.g. n-grams, gender-coded language), and national HE markers of esteem (REF21, TEF23, NSS24) were analysed. Applicant grades and priorities produced the strongest effects on genAI outputs. Higher-grade applicants and those prioritising research received significantly more masculine-coded language, independent of applicant gender. N-gram patterns also diverged: high-grade prompts more frequently elicited terms relating to excellence and research intensity, whereas lower-grade prompts produced greater emphasis on widening access. Recommendations were systematically skewed, with higher grades, private schooling, and research-focused priorities increasing the likelihood of recommending elite institutions and programmes with higher entry requirements. Critically, the gender-coded language of outputs predicted institutional characteristics: masculine-coded responses were associated with recommendations featuring higher entry thresholds and stronger research performance, while feminine-coded responses favoured institutions with higher student satisfaction. These findings reveal clear, systematic biases in how genAI guides prospective HE applicants. Such biases risk reinforcing existing educational and socioeconomic inequalities, underscoring the need for transparency, regulation, and oversight in the use of genAI within HE decision-making. HighlightsO_LIGenAI is widely used by HE applicants despite little study of its biases. C_LIO_LI216 prompts across 3 chatbots generated 3240 programme suggestions. C_LIO_LIGrades and priorities drove major shifts in language and recommendations. C_LIO_LIGender-coded wording mapped onto research strength and entry standards. C_LIO_LIGenAI biases may reinforce inequalities in HE admissions decision-making. C_LI

TAR DNA-binding protein 43 (TDP-43) aggregation is a hallmark of several neurodegenerative diseases, including amyotrophic lateral sclerosis and frontotemporal dementia. Recent therapeutic efforts have highlighted the potential of small molecules capable of inhibiting TDP-43 aggregation; however, no effective treatments currently exist. Here, we developed a hybrid machine learning approach combining graph neural network (GNN) embeddings with traditional chemical descriptors and biological target annotations. Using XGBoost as the final classifier enabled model interpretability through SHAP analysis, allowing the identification of key chemical features and target annotations associated with TDP-43 anti-aggregation activity. Complementary Monte Carlo Tree Search analysis highlighted specific chemical substructures linked to predicted activity. By screening an external library of 3,853 small molecules, the model identified two compounds not previously evaluated against TDP-43 aggregation, namely berberrubine and PE859. Molecular docking analysis revealed that both compounds interact favourably with the TDP-43 RNA recognition motif (RRM) domain through distinct binding modes. Experimental validation showed that both compounds significantly reduced TDP-43 aggregation in HEK cells. Further testing in Caenorhabditis elegans expressing human TDP-43 demonstrated that PE859 significantly rescued locomotor defects, while berberrubine showed partial improvement. This work establishes a hybrid machine learning approach for accelerating small molecule drug discovery, yielding two promising therapeutic candidates for TDP-43 proteinopathies.

Applying deep learning models to RNA-Seq data poses substantial challenges, primarily due to the high dimensionality of the data and the limited sample sizes. To address these issues, this study introduces an advanced deep learning pipeline that integrates feature engineering with data augmentation. The engineering application focuses on biomedical engineering, specifically the classification of RNA-Seq datasets for disease diagnosis. The proposed framework was initially validated on synthetic datasets generated from Naive Bayes, where MLP-based augmentation yielded a notable improvement in predictive performance. Building on this foundation, we applied the approach to chromophobe renal cell carcinoma (KICH) RNA-Seq data from The Cancer Genome Atlas (TCGA). Following standard preprocessing steps normalization, transformation, and dimensionality reduction, the analysis concentrated on three main aspects: augmentation strategies, preprocessing methods, and explainable AI (XAI) techniques in relation to classification outcomes. Feature selection was performed through PCA, Boruta, and RF-based methods. Three augmentation strategies linear interpolation, SMOTE, and MixUp were evaluated. To maintain methodological rigor, augmentation was applied exclusively to the training set, while the test set was held out for unbiased evaluation. Within this framework, we conducted a comparative assessment of multiple deep learning architectures, including MLP, GNN, and the recently proposed Kolmogorov-Arnold networks (KAN). The GNN achieved the highest classification accuracy (99.47%) when trained with MixUp augmentation combined with RF feature selection, and achieved the best F1 score (0.9948). Consequently, the GNN-based XAI framework was applied to the RF dataset enriched with MixUp. XAI analyses identified the top 20 most influential genes, such as HNF4A, DACH2, MAPK15, and NAT2, which played the greatest role in classification, thereby confirming the biological plausibility of the model outputs. To further validate model robustness, cervical cancer and Alzheimers RNA-Seq datasets were also tested, yielding consistent and reliable results. Overall, the findings highlight the value of incorporating data augmentation into deep learning models for RNA-Seq analysis, not only to improve predictive performance but also to enhance biological interpretability through explainable AI approaches.

This paper describes MMP, a three-stage framework for systematic quantum optimization of constrained molecular docking problems. The protocol addresses the "formulation bottleneck"--the critical challenge of translating constrained optimization problems into valid QUBO (Quadratic Unconstrained Binary Optimization) formulations for quantum solvers. MMP replaces heuristic penalty tuning with data-driven calibration through: (1) classical solution-space analysis to validate fragment libraries before quantum deployment, (2) systematic penalty sweeps to identify optimal "Goldilocks Zone" coefficients, and (3) MAC-QAOA (MMP Adaptive Constraint QAOA) with layer-dependent penalty decay. Preliminary benchmarks on synthetic constrained optimization problems demonstrate 99.7% solution validity at identified elbow points and 25.5% improvement in solution quality over static-penalty QAOA. MMP is hardware-agnostic but designed for near-term devices including Pasqals Orion Gamma (140+ qubits). The theoretical framework, algorithmic details, and preliminary validation results of the protocol are discussed, establishing a systematic methodology for quantum-augmented optimization workflows for drug discovery. All benchmarks are conducted on synthetic constrained optimization instances that reproduce structural features of docking formulations; application to real molecular docking targets is left for future work.

Knowledge graphs (KGs) have become an important asset in biomedical research and drug discovery by enabling the structured integration of heterogeneous biological knowledge. When combined with machine learning (ML), KGs support the identification of novel drug-target relationships, but existing approaches are often KG-centric, relying primarily on graph structure and embeddings while overlooking disease-specific biological and clinical context. Moreover, many high-impact applications depend on proprietary KG infrastructures, limiting accessibility for the broader research community. Here, we introduce Artemis, a practical and generalisable machine-learning framework for indication-aware target prioritisation that integrates public biomedical KGs with clinical evidence from the ChEMBL database. Artemis derives graph-based representations of clinically validated drug targets from multiple publicly available KGs and augments them with disease-relevant clinical features from ChEMBL. This hybrid feature space is used to train supervised ML models across seven disease indications, with performance assessed via cross-validation and guided parameter optimisation. The framework is further evaluated on emerging breast cancer targets reported at the San Antonio Breast Cancer Symposium 2024, demonstrating its ability to prioritise novel candidates. Overall, this work demonstrates that publicly available KGs can be used for actionable, translational target discovery when coupled with clinical data. Artemis provides an accessible, scalable, and cost-efficient alternative to proprietary KG platforms. Thereby offering a practical solution for researchers seeking to prioritise therapeutic targets in real-world drug discovery settings. Key PointsO_LIKG applications can support the identification of novel drug-target relationships but rely primarily on graph structure while overlooking disease-specific biological and clinical context. C_LIO_LIArtemis performs indication-aware target prioritisation that integrates public biomedical KGs with clinical evidence from the ChEMBL database. C_LIO_LIArtemis is evaluated on emerging breast cancer targets reported at the San Antonio Breast Cancer Symposium 2024, demonstrating its ability to prioritise novel candidates. C_LIO_LIArtemis provides an accessible, scalable, and cost-efficient alternative to proprietary KG platforms offering a practical solution for researchers seeking to prioritise therapeutic targets in real-world drug discovery settings. C_LI

Drug response prediction models are widely used to nominate biomarkers and guide preclinical drug prioritization. However, their reported performance hinges on rigorous separation of training and test data during cross-validation (CV). Here we show that a commonly used pattern--supervised feature screening performed on the full dataset before CV--introduces data leakage that systematically underestimates prediction error. Analyzing 265 drugs across 1,462 cancer cell lines, we find that leakage-free CV increases mean squared error (MSE) by 16.6%, with low feature-set overlap between leaked and leakage-free pipelines (mean Jaccard 0.18). A manual audit of 12 recent deep learning and classical methods found confirmed leakage in 10. Such inflated performance estimates likely contribute to computational predictions that fail during independent validation or experimental follow-up. We provide an audit guide and reference implementation to prevent leakage, and introduce a tissue-aware Data Shared Elastic Net (DSEN) that, under correct evaluation, improves prediction for 65.7% of drugs while yielding sparser, more targeted biomarker sets.

Chemical toxicity assessment is critical for drug development and environmental safety. Computational models have emerged as a promising alternative to animal testing and now play a significant role in efficiently evaluating new chemicals. To address the urgent need for providing user-friendly machine learning tools in computational toxicology, we developed ToxiVerse, a public web-based platform. It provides curated toxicity datasets, automatic chemical bioprofiling, and a predictive modeling interface designed for researchers who lack programming expertise. The platform comprises three integrated modules: (i) the Bioprofiler module, which provides chemical descriptors by combining chemical-bioactivity data from PubChem assay with a machine learning-based data gap-filling procedure; (ii) the Database module, which hosts around 50,000 curated unique chemicals covering diverse toxicity endpoints; and (iii) the Cheminformatics module, which allows users to upload their own datasets, use datasets from ToxiVerse, or retrieve existing data from PubChem; perform chemical curation; and automatically generate Quantitative Structure-Activity Relationship (QSAR) models to predict chemicals of interest. ToxiVerse enables researchers to carry out bioprofiling, access curated toxicity datasets, and evaluate chemical toxicity through machine learning-based modeling and prediction. The platform is supported by sample files and a detailed tutorial, and it is freely accessible at www.toxiverse.com. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/708255v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@d92764org.highwire.dtl.DTLVardef@a92f4aorg.highwire.dtl.DTLVardef@15fa39corg.highwire.dtl.DTLVardef@1ee89bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

G protein-coupled receptors (GPCRs) play an ubiquitous role in the transduction of extracellular stimuli into intracellular responses and therefore represent a major target for the development of novel peptide-based therapeutics. In fact, approximately 30% of all non-sensory GPCRs are peptide-targeted, representing a blueprint for the design of de novo peptides, both as pharmacological tools and therapeutics. The recent advances of deep learning-based protein structure generation and structure prediction offer a multitude of peptide design stategies for GPCRs, yet confidence metrics rarely correlate with experimental success. In the context of peptides, this problem is exacerbated due to the lack of elaborate tertiary structures in peptides, raising the question of whether this is due to inadequate sampling or insufficient scoring. In this two-part benchmark, we addressed this question by first simulating the validation process of 124 unique known GPCR-peptide complexes using AlphaFold2 Initial Guess, Boltz-2 and RosettaFold3. We then assessed the peptide sampling capabilities of the respective generative methods BindCraft, BoltzGen and RFdiffusion3. Our results indicate that current design pipelines primarily suffer from significant confidence overestimation for misplaced peptides in the validation phase across all three prediction methods. We further highlight occurrences of significant memorization in both prediction as well as generation of peptides. While all generative methods sample backbone space sufficiently, their simultaneous sequence generation remains subpar and can be partially recovered through the use of ProteinMPNN. Taken together, our benchmark offers guidance for the design of peptides specifically using deep learning-based pipelines. Autor summaryDeep learning-based protein design is revolutionizing computational biology and development of such tools is progressing rapidly with increasing attention from both academic and non-academic institutions. Their applicability and performance is often assessed from an all-purpose objective, with implicit bias towards larger protein-protein interactions. Due to their size, peptides therefore present an edge case where performance is known to decrease compared to larger, more structured proteins. Here, we present a benchmark specifically for the deep learning-based design of peptides targeting G protein-coupled receptors (GPCRs), a major therapeutic drug target family, assessing the generation of novel GPCR-targeting peptides and the validation of these designs separately. Our results show that generative methods sample potential peptide placements and orientations sufficiently but validation fails to differentiate valid from invalid designs, indicating that the so-called scoring problem remains unsolved. Although focusing on a specific use-case, our results are generalizable to the broader field of protein design. Consequently, it can offer guidance for peptide-specific design applications and can contribute to the development and improvement of new methods.

Recombinant human Interleukin-2 (rhIL-2, Aldesleukin) is used in immunotherapy for metastatic melanoma and renal cell carcinoma. Low-dose IL-2 has been investigated for administration after adoptive T cell transfer to enhance CAR T expansion and sustain effector function. However, systemic IL-2 can cause severe toxicities and promote expansion of regulatory T cells (Tregs). Previous attempts at mitigating cytokine-mediated side effects involved isolating CAR T cell signaling from endogenous immune responses by developing IL-2/IL-2R{beta} based selective ligand-receptors systems. Expressing these variant orthogonal (ortho)IL2-R{beta} receptors in CAR T cells and supplying variant orthoIL-2, was shown to dramatically improve selectivity in CAR T cell expansion and anti-tumoral potency in a leukemia mouse model. This study describes the computational design of synthetic orthogonal cytokine receptor-ligand systems based on the scaffolds of the human canonical IL-2 and IL-2R{beta}. Leveraging state-of-the-art AlphaFold3 (AF3) structure prediction capabilities and a physics-informed constrained sequence generator (CSG), the pipeline generates, filters and ranks sets of putative orthoIL-2/orthoIL-2R{beta} mutant designs. Variants displaying minimal predicted off-target interactions and enhanced in target contacts are prioritized for structural modelling. Top designs showed outstanding AF3 structural and interfacial quality metrics ipTM and pTM, with averages between cognate pairs of 0.724{+/-}0.05 and 0.770{+/-}0.042, respectively. All in-silico hits showed ipTM <0.5 for non-cognates, indicating a good likelihood of orthogonality. Additionally, putative hits showed high levels of predicted structural fidelity to wild-type (WT) human IL-2/IL-2R{beta} (PDB: 2ERJ), with an average structural root-mean-square deviation (RMSD) of 0.843{+/-}0.375 [A]. These mutants incorporated 7-26 interfacial mutations derived from multiple interface selection strategies. Altogether, the results support the putative foldability and selective affinity of top-ranking mutants displaying metrics close-to or within experimental reference range. Finally, strengths and limitations are discussed, alongside the experimental implications of coupling a constrained protein design pipeline to the discovery and validation of selective binders based on naturally occurring scaffolds.

MotivationPhenotypic drug discovery generates rich multi-modal biological data from transcriptomic and morphological measurements, yet translating complex cellular responses into molecular design remains a computational bottleneck. Existing generative methods operate on single modalities and condition on post-treatment measurements without leveraging paired control-treatment dynamics to capture perturbation effects. ResultsWe present Pert2Mol, the first framework for multi-modal phenotype-to-structure generation that integrates transcriptomic and morphological features from paired control-treatment experiments. Pert2Mol employs bidirectional cross-attention between control and treatment states to capture perturbation dynamics, conditioning a rectified flow transformer that generates molecular structures along straight-line trajectories. We introduce Student-Teacher Self-Representation (SERE) learning to stabilize training in high-dimensional multi-modal spaces. On the GDP dataset, Pert2Mol achieves Frechet ChemNet Distance of 4.996 compared to 7.343 for diffusion baselines and 59.114 for transcriptomics-only methods, while maintaining perfect molecular validity and appropriate physicochemical property distributions. The model demonstrates 84.7% scaffold diversity and 12.4 times faster generation than diffusion approaches with deterministic sampling suitable for hypothesis-driven validation. AvailabilityCode and pretrained models will be available at https://github.com/wangmengbo/Pert2Mol.

Generative structure-based drug design (SBDD) models have shown great promise to accelerate our ability to discover novel drug candidates. However, these models have been criticized for producing compounds that are not very synthesizable, and therefore not practically applicable to drug design. In this work, we propose a way to circumvent the synthesizability issue by introducing a model-guided virtual screening (MGVS) pipeline which pairs SBDD models with efficient chemical similarity search methods to identify synthesizable analogs of generated compounds in existing ultra-large compound databases. Using this approach, we demonstrate that synthesizable analogs of generated compounds with equivalent or better docking scores and similar predicted binding poses can be reliably identified across a wide range of protein targets. We find that MGVS outperforms standard virtual ligand screening (VLS), consistently yielding at least a 25x improvement in screening efficiency across three different SBDD models. As drug-like chemical spaces continue to grow and standard VLS methods focused on exhaustive screening become increasingly impractical, approaches like MGVS that effectively narrow the search space will become critical for advancing drug discovery.

MotivationMolecular representation learning is central to computational drug discovery. However, most existing models rely on single-modality inputs, such as molecular sequences or graphs, which capture only limited aspects of molecular behaviour. Yet unifying these modalities with complementary resources such as textual descriptions and biological interaction networks into a coherent multimodal framework remains non-trivial, hindering more informative and biologically grounded representations. ResultsWe introduce SELFormerMM, a multimodal molecular representation learning framework that integrates SELFIES notations with structural graphs, textual descriptions, and knowledge graph- derived biological interaction data. By aligning these heterogeneous views, SELFormerMM effectively captures complementary signals that unimodal approaches often overlook. Our performance evaluation has revealed that SELFormerMM outperforms structure-, sequence-, and knowledge-based models on multiple molecular property prediction tasks. Ablation analyses further indicate that effective cross-modal alignment and modality coverage improve the models ability to exploit complementary information. Overall, integrating SELFIES with structural, textual, and biological context enables richer molecular representations and provides a promising framework for hypothesis-driven drug discovery. AvailabilitySELFormerMM is available as a programmatic tool, together with datasets, pretrained models, and precomputed embeddings at https://github.com/HUBioDataLab/SELFormerMM. Contacttuncadogan@gmail.com

Current in silico drug discovery protocols ubiquitously depend on lead generation using a ligand-based approach in which novel leads are generated by fragment-signature matching or by a structure-based search involving molecular docking and conformational dynamics. None of them incorporates cellular contexts in which these drugs ultimately operate, leaving the task to a later stage of optimization leading to a high failure rate. Incorporating systems-level responses of drugs in an early stage of lead generation can significantly address this concern but has not been sufficiently explored. In this work, we employ a systems-level approach using connectivity map (CMAP) library to generate leads against a challenging system of a TLR pathway. Starting with gene expression data of TLR5 activation by its natural ligand, we generated molecular leads using CMAP and rigorously analyzed their validity using ligand and structure-based approaches, and helping to prioritize top hits. Experimental validation using ELISA-based antibody assay confirmed the activation of TLR5 by each of the top nine prioritized leads with their dose-dependent patterns suggesting that some of them may actually interact with the TLR signaling pathway in a complex manner. Although, demonstrated on TLR5, the proposed framework is intuitively scalable to other lead generation and optimization tasks.

Developing scientific reading skills is critical for undergraduate STEM students due to scientific literatures unique formatting and use of specialized jargon. Generative AI tools such as ChatGPT offer students the ability to ask questions about what they are reading interactively. Previously, we reported the development of a ChatGPT-assisted reading guide that combined structured, active reading strategies with using ChatGPT to clarify unfamiliar words and concepts in real time. In the initial study, undergraduates found the use of the ChatGPT-assisted reading guide helpful in their understanding of an abstract and introduction of a journal article. Here, the ChatGPT-assisted reading guide was used in a journal club assignment for an undergraduate chemistry course. ChatGPT transcripts were analyzed for common types of interactions, and students were surveyed about their experience. Overall, students reported that using the ChatGPT-assisted reading guide was helpful in understanding the article and helped them have more productive class discussions. However, some students also expressed skepticism about using AI tools, citing concerns about accuracy of AI-generated information and the effect of using AI on their own learning.

Recently developed deep learning-based tools can effectively generate structural models of complexes of proteins and non-proteinaceous compounds. While some of their predictive capabilities are truly exciting, others remain to be thoroughly tested. Here, we probe whether the ligand input format (Chemical Component Dictionary, CCD, or Simplified Molecular Input Line Entry System, SMILES) and charge (which depends on protonation) will affect the results of the predictions by four popular algorithms: AlphaFold 3, Boltz-2, Chai-1, and Protenix-v1. We chose methylamine and acetic acid as two of the simplest titratable chemicals that are omnipresent in proteins as amino and carboxy moieties, and are consequently ubiquitous in the Protein Data Bank models that are most commonly used for training. Unexpectedly, we found that for both molecules, in many cases the input format affected the prediction results, and did it much stronger compared to protonation, whereas changes in the formally specified charge of the molecules did not lead to changes in binding expected from experiments. We conclude that (i) ensuring identical results irrespective of input formats and (ii) inclusion of protonation-related steps into training and prediction pipelines are the two available paths for improvement of protein-ligand structure prediction algorithms.