Bioinformatics Tools Explorer

Searches scientific literature, identifies relevant studies, helps screen papers, extracts structured information, compares studies in tables, summarizes evidence and assists with literature review workflows. Particularly useful for systematic-style evidence tables and research surveys.

Ask natural-language scientific questions and retrieve evidence from research papers. Useful for rapidly determining what the literature says about a hypothesis, intervention, mechanism or biological relationship.

Uses Smart Citations to examine whether later publications support, contrast or merely mention a study. Particularly valuable for checking controversial claims, evaluating evidence strength and verifying whether important findings have been replicated. (scite.ai)

Explain difficult papers, ask questions about PDFs, simplify scientific terminology, summarize methods and results, discover related literature and assist in understanding complex research articles.

Start with seed papers and discover related articles, authors, citation networks and research clusters. Very useful for finding connected literature and understanding how a research field has evolved. (ResearchRabbit)

Generate graphical maps of related research, identify foundational studies, discover neighbouring research areas and explore clusters of papers around a topic.

Create evolving literature maps, trace citation relationships, identify new publications and maintain a living literature review. Particularly useful for long-term PhD or research projects.

Search a large scientific literature corpus, discover influential papers, obtain recommendations, explore citations and identify related work. Useful as a free foundation for literature discovery.

Search and analyse publications, authors, institutions, concepts and citation networks. Particularly useful for programmatic literature surveys, bibliometrics and building custom AI/ML literature-analysis pipelines.

Performs retrieval, ranking, synthesis and citation-supported scientific question answering. Useful for generating literature-grounded answers and exploring scientific questions using research papers.

Useful for exploratory literature searches, identifying recent papers and reports, generating research overviews and locating scientific sources. Scientific claims should still be checked against primary publications.

Upload papers and PDFs and ask questions across your own curated collection. Useful for comparing multiple papers, identifying recurring themes, generating research briefs and maintaining a controlled literature-review workspace.

Analyse uploaded research papers, compare methodologies, extract biological mechanisms, identify limitations and synthesize information across multiple documents. Best used with supplied/verified papers rather than as the sole literature search engine.

Can assist in developing search strategies, comparing papers, extracting biological mechanisms, organizing literature-review tables, synthesizing findings and generating research frameworks. All citations and scientific claims should be independently verified.

Analyse long documents, compare papers, summarize scientific literature and assist in research synthesis. Can be particularly useful when working across documents and other Google ecosystem resources.

Generates structured summaries of papers, extracts key findings and helps researchers rapidly triage large numbers of articles before detailed reading.

Supports literature-based academic writing, manuscript editing, scientific language improvement and research-paper preparation. Useful during the transition from literature survey to manuscript writing.

Helps discover research literature, explore concepts and identify connections between scientific ideas. Useful for broad interdisciplinary literature exploration.

Starting from one or more seed papers, identify important related publications, citation relationships and key works within a research area.

Conduct broad literature surveys while connecting scientific publications to grants, patents, datasets and clinical trials. Particularly valuable for translational and drug-discovery research.

Search scientific literature and patents together, making it highly useful for biotechnology, drug discovery, translational research and identifying commercial applications of scientific discoveries.

Upload a paper and select difficult passages for explanation. Useful when reading papers outside your primary area of expertise or understanding complex methods and terminology.

Search for papers, generate topic-based literature summaries and identify relevant research in specific scientific areas.

Upload text or enter a research topic to discover relevant academic literature based on semantic similarity. Useful for discovering papers outside obvious keyword matches.

Supports screening and organization of large literature collections for systematic reviews. Useful for title/abstract screening, inclusion/exclusion decisions and collaborative review workflows.

Manage systematic reviews, screen studies, remove duplicates, extract data and coordinate multiple reviewers. Particularly suitable for formal evidence reviews.

Uses active learning to prioritize relevant records during literature screening, reducing the number of papers that must be manually reviewed before finding relevant studies.

Search and synthesize evidence from scientific literature, particularly for research questions requiring evidence-based answers.

Explore scientific topics using natural-language questions, identify relevant literature and investigate connections among concepts and publications. Particularly useful when institutional access to Scopus is available.

Supports natural-language research queries, literature discovery, topic exploration and identification of relevant research directions within indexed scholarly literature.

Store papers, organize literature libraries, manage citations and connect with AI literature-analysis tools. It is not itself primarily an LLM literature engine but is extremely useful as the central reference-management layer.

Organize papers, annotate PDFs, manage references and build literature libraries for downstream AI-assisted analysis.

AI agents are designed to search, interpret and synthesize scientific literature and assist researchers with complex scientific questions and hypothesis exploration.

Uses semantic and AI-assisted search approaches to identify relevant scientific papers, particularly for complex research questions that are difficult to express with traditional Boolean keywords.

Helps discover papers, organize references, analyse documents, generate summaries and assist with literature-review workflows.

Assists with literature-based academic writing, drafting and citation-aware writing workflows. Most useful after literature collection and synthesis.

Helps improve scientific writing, academic language, manuscript wording and consistency based on scholarly language patterns.

Upload multiple research papers and ask questions, extract information and summarize documents. Useful for quickly interrogating a literature collection.

Ask questions directly across uploaded research papers, extract methodology, results and conclusions, and compare information between documents.

Used to predict how candidate drugs may be metabolized by CYP enzymes. Useful during early drug discovery for identifying potential metabolic liabilities, predicting CYP-mediated drug metabolism, anticipating drug–drug interactions, comparing candidate compounds, and supporting ADMET analysis. It can help researchers prioritize compounds before expensive experimental metabolism assays. (PubMed Central (PMC))

Can be used to investigate protein–compound interactions, evaluate candidate ligands against biological targets, prioritize compounds from virtual screening, compare predicted binding relationships, and support computational drug discovery pipelines. It is particularly relevant when integrating gene/target prioritization with downstream compound screening. (PubMed Central (PMC))

Used to design synthetic routes for drug-like molecules while considering properties relevant to absorption, distribution, metabolism, excretion, and toxicity. Applications include retrosynthetic route generation, prioritizing synthesizable compounds, identifying chemically feasible pathways, and integrating medicinal chemistry with predicted pharmacological properties. (PubMed Central (PMC))

Can be applied to identify potential biosynthetic or synthetic routes for small molecules, natural products, and biologically active compounds. Useful in synthetic biology, metabolic engineering, natural-product discovery, pathway reconstruction, and designing routes to produce candidate therapeutic molecules. (PubMed Central (PMC))

Used to interpret non-coding variants, predict disruption or creation of transcription-factor binding sites, investigate regulatory mutations, prioritize potentially functional SNPs, and connect genetic variation with altered gene regulation. Particularly useful for GWAS interpretation and regulatory genomics. (PubMed Central (PMC))

Can generate protein representations from protein language models and support downstream prediction tasks. Applications include protein-function prediction, classification of protein sequences, representation learning, custom ML model development, analysis of poorly characterized proteins, and reducing the computational barrier associated with generating protein-language-model embeddings locally. (ScienceDirect)

Useful for asking integrated questions involving drugs, genes, proteins, diseases, pathways and therapeutic targets. It connects resources such as Open Targets, CTD, STRING, UniProt, ClinVar, Reactome and others. Particularly useful for biological knowledge retrieval, hypothesis generation, drug–target exploration and rapid evidence synthesis. (BioChirp)

Used to investigate transcription-factor binding, chromatin accessibility, histone modifications and regulatory genomic regions. Applications include identifying potential regulators of a gene set, interpreting differentially expressed genes, connecting DEGs with transcription factors, studying enhancer/promoter regulation and generating features for AI/ML models of gene regulation. (PubMed Central (PMC))

Used to investigate tumor-associated antigens and epitopes, support cancer vaccine research, identify candidate immunotherapy targets, analyze tumor antigen recognition and develop computational models for epitope prioritization. Particularly useful for integrating cancer genomics with immunological prediction. (PubMed Central (PMC))

Provides an integrated web environment for analysing biological datasets without requiring extensive local computational infrastructure. Useful for genomics workflows, structural biology analysis, dataset management, reproducible biological analysis and centralized project organization. It is particularly relevant for researchers who want web-accessible computational workflows rather than installing individual bioinformatics tools. (IBDC)

QSAR modelling; toxicity prediction; molecular property prediction; drug–target modelling; generative chemistry.

Develop and compare models for drug discovery, target prediction, ADMET, drug response and clinical ML.

Train ML models for compound–target prediction; QSAR; drug repurposing; bioactivity modelling.

Train binding-affinity models; predict drug–target interactions; validate docking and ML predictions.

Compound similarity analysis; bioactivity retrieval; AI training datasets; chemical annotation.

Drug repurposing; drug–target network analysis; therapeutic target identification.

Compound–gene interaction analysis; toxicogenomics; disease mechanism research; drug prioritization.

Identify compound targets; network pharmacology; drug repurposing; chemical biology analysis.

Predict binding between drugs and proteins; virtual screening; drug repurposing.

Predict quantitative binding affinity; screen compounds against biological targets.

Molecular embeddings; property prediction; similarity modelling and virtual screening.

Predict chemical properties; molecular embeddings; QSAR modelling and chemical similarity analysis.

Generate novel compounds optimized for target activity, physicochemical properties and other objectives.

Predict protein–ligand binding poses; accelerate virtual screening and structural drug discovery.

Train disease classifiers; identify biomarkers; perform ML-based gene-expression classification and differential analysis.

Dataset retrieval for transcriptomic ML, cross-study analysis and biomarker discovery.

Tissue-specific expression modelling; eQTL prediction; AI models of gene regulation.

Cancer classification; survival prediction; biomarker discovery; multi-omics ML; mutation and expression modelling.

Comparative cancer genomics; mutation classification; AI-based cancer subtype discovery.

Explore mutations, CNVs and expression; build predictive models; investigate molecular cancer subtypes.

Predict essential genes; discover therapeutic targets; model drug sensitivity.

Train regulatory genomics models; study transcription-factor binding; chromatin accessibility prediction.

Epigenetic ML modelling; disease-associated regulatory analysis; chromatin-state prediction.

Train cell annotation models; discover cell states; construct disease-vs-normal cell maps.

Single-cell ML benchmarking; cell-type annotation; gene-expression representation learning.

Reference-based cell annotation; marker discovery; training cell-type classifiers.

Cell-type comparison; reference atlas construction; ML-based transcriptomic analysis.

Metabolite identification; metabolic biomarker discovery; ML-based disease classification.

Train metabolomic classifiers; biomarker prediction; multi-omics integration.

Metabolite annotation; spectral clustering; ML-based metabolomics and natural-product discovery.

Compound identification; spectral ML model development; metabolomic annotation.

Microbiome-based disease classification; metagenomic ML; microbial biomarker discovery.

Microbial classification; metagenomic functional prediction; microbiome AI modelling.

Comparative genomics; microbial functional prediction; environmental ML studies.

Systems biology modelling; AI-assisted parameter optimization; dynamic biological network analysis.

Identify likely 3D structures of proteins; study domains, active sites and structural regions; support functional annotation; investigate disease-associated mutations; perform structure-based drug discovery; select structures for molecular docking.

Predict protein structures and biomolecular complexes; investigate protein–protein, protein–DNA and protein–ligand interactions; support structural biology hypotheses.

Study protein complexes, protein–ligand binding, protein–DNA interactions and protein–RNA interactions; generate structural hypotheses for molecular mechanisms.

Predict protein structures from amino-acid sequences; analyze difficult proteins; generate structural models for functional and evolutionary studies.

Predict monomer and protein-complex structures; perform rapid structural screening; generate models without maintaining extensive local infrastructure.

Construct 3D protein models using homologous structures; investigate mutations; prepare structures for docking and molecular simulations.

Rapid prediction of protein structures; proteome-scale structural analysis; studying proteins with limited evolutionary information.

Explore structurally predicted proteins from uncultured organisms; discover novel protein families; investigate environmental biodiversity.

Predict structures directly from protein sequences; structural biology research; comparison of alternative AI structure prediction methods.

Predict effects on transcription-factor binding, chromatin marks and gene regulation; prioritize regulatory variants.

Predict gene regulation, chromatin accessibility and expression-related genomic features.

Predict effects of sequence variation on gene expression and regulatory activity; interpret non-coding variants.

Study regulatory variants, chromatin biology and molecular effects of genomic mutations.

Rapid prioritization of potentially functional variants, including variants outside protein-coding regions; genome-wide mutation interpretation. (The Verge)

Predict chromatin features; analyze regulatory sequences; investigate effects of mutations.

Call SNPs and indels from sequencing reads; improve variant discovery from whole-genome and exome data.

Variant detection from Oxford Nanopore and PacBio sequencing; genome analysis using long reads.

Epigenomic profiling; methylation detection; study of disease-associated epigenetic alterations.

Learn cellular states; identify disease-associated genes; predict perturbation responses; prioritize therapeutic targets.

Cell embedding; cell-type annotation; integration of single-cell datasets; perturbation modelling.

Cell-state representation; cross-dataset integration; disease modelling; gene-expression prediction.

Cell embedding, batch integration, annotation and biological state modelling.

Automated cell-type annotation; representation learning; transfer learning between datasets.

Batch correction; dimensionality reduction; integration; differential expression; multimodal analysis.

Rapid annotation of scRNA-seq cell populations using reference models.

Assign cell identities by comparing transcriptomes with reference datasets.

Map disease datasets to healthy reference atlases; compare cohorts; transfer cell-state annotations.

RNA sequence representation; structure/function prediction; RNA classification and downstream modelling.

Protein embedding generation; function prediction; subcellular localization; remote homology detection; mutation analysis.

Generate protein embeddings; predict structural and functional properties; protein design and sequence analysis.

Predict Gene Ontology functions; annotate poorly characterized proteins; integrate structural information with functional prediction.

Predict Gene Ontology terms directly from sequence; genome-scale functional annotation; prioritize candidate proteins.

Prioritize disease-associated variants; identify potentially pathogenic amino-acid substitutions; assist clinical and genetic research.

Variant prioritization in exome/genome studies; interpretation of disease-associated mutations.

Score coding and non-coding variants; prioritize mutations in genome sequencing studies; support disease-gene discovery.

Identify splice-altering variants; interpret non-coding mutations; prioritize disease-causing genetic changes.

AI-supported target prioritization; drug target discovery; disease–gene association analysis.

Phenotype-based disease-gene prediction; rare disease diagnosis; graph-based ML.

Disease gene prioritization; network medicine; graph neural-network models.

Disease mechanism exploration; gene prioritization; AI-based disease association modelling.

Drug repurposing; drug–target ML; pharmacological network analysis.

Graph-based drug repurposing; disease–gene prediction; network ML research.

Knowledge-graph embeddings; drug repurposing; precision medicine modelling.

Train protein language models; function prediction; protein classification; sequence-based ML.

Graph neural networks; network medicine; pathway prediction; gene prioritization.

Train interaction-prediction models; construct gene and protein networks.

Protein interaction modelling; network reconstruction; AI-based interaction prediction.

Pathway enrichment; graph-based pathway analysis; disease pathway modelling.

ML-assisted pathway modelling; gene-set interpretation; metabolic network analysis.

Pathway enrichment; network analysis; AI knowledge-graph construction.

Functional prediction; ontology-aware ML; protein/gene annotation models.

Feature generation for protein ML; functional annotation; domain prediction.

Protein classification; domain-based ML; evolutionary modelling.

Train structural ML models; validate AI structure predictions; molecular docking and protein engineering.

Structure–function modelling; AI feature generation; protein functional interpretation.