
Restore missing residues in antibody sequences using a language model trained on the Observed Antibody Space (OAS) database. Achieves better restoration than IMGT germlines or ESM-1b while being 7x faster.

Antibody-specific language model for predicting non-germline residues (NGL) in antibody sequences. AbLang-2 addresses germline bias in existing antibody language models by focusing on somatic hypermutation patterns, enabling more accurate prediction of amino acid likelihoods and generation of context-aware embeddings for antibody sequences.

Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.

Predict 22 ADMET properties from SMILES strings with the upstream Admetica Chemprop models from Datagrok.

Cluster Multiple Sequence Alignments to predict alternative protein conformations with AlphaFold2. Uses DBSCAN clustering to identify sequence subgroups.

Faithful static-mode Aggrescan3D wrapper for per-residue aggregation propensity analysis from a single protein structure.

Calculate the aliphatic index of protein sequences. A measure of the relative volume occupied by aliphatic side chains, indicating thermostability.

Predict metal and water binding sites in protein structures using 3D convolutional neural networks (AllMetal3D + Water3D).

Generate protein conformational ensembles with ESMFlow, the single-sequence AlphaFlow model family. Produces multiple diverse structures showing protein flexibility and dynamics.

AlphaFold2 via ColabFold for high-accuracy protein structure prediction. Uses MMSeqs2 API for MSA generation with no local databases required. Supports monomer and multimer prediction.

AlphaGenome predicts variant effects on gene expression by comparing reference and alternate alleles. Analyze how genetic variants impact regulatory function across up to 1M base pair regions. Uses your own DeepMind API key - no credit cost.

Analyze amino acid composition of protein sequences. The tool accepts FASTA sequences and outputs the percentage of each amino acid in the sequence.

Number antibody and T cell receptor variable domain sequences using multiple numbering schemes (IMGT, Chothia, Kabat, Martin, AHo, Wolfguy). Identifies chain type, species, and assigns germline genes.

Inverse folding for antibody variable domains and nanobodies. Predicts amino acid sequences compatible with antibody structures using IMGT numbering while preserving upstream AntiFold chain handling and structural constraints.

AutoDock Vina is a widely-used molecular docking tool that predicts protein-ligand binding modes using physics-based force fields. Fast, reliable, and the gold standard for structure-based drug discovery.

GPU-accelerated molecular docking using the AutoDock4 force field. Up to 56x faster than serial AutoDock via CUDA parallelization of the Lamarckian Genetic Algorithm.

Design de novo protein binders using AlphaFold2 backpropagation, ProteinMPNN sequence optimization, and PyRosetta relaxation. BindCraft generates novel protein sequences that bind to user-specified target surfaces.

Antibody humanization and humanness evaluation platform from Merck. Sapiens mode uses deep learning trained on the Observed Antibody Space (OAS) to humanize antibody sequences, while OASis mode evaluates humanness using 9-mer peptide search against human antibody databases.

Boltz-2 is a biomolecular foundation model for structure and binding affinity prediction. Supports proteins, ligands, DNA, and RNA in multi-component complexes. Automatically scales GPU resources for large complexes. Predicts binding affinity with near-FEP accuracy at 1000x faster speed.

BoltzGen is a state-of-the-art AI model for designing protein and peptide binders against any biomolecular target. Using generative diffusion models, it creates novel binders (proteins, peptides, nanobodies) with nanomolar-level binding affinity.

Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns

Chai-1 is a multi-modal foundation model for molecular structure prediction. Predicts 3D structures for proteins, ligands, DNA, RNA, and multi-component complexes with high accuracy.

Predict protein secondary structure using the classic Chou-Fasman algorithm based on amino acid propensities

Official CleaveNet wrapper for matrix metalloproteinase cleavage prediction and peptide generation. Predict cleavage z-scores plus uncertainty across 17 MMP variants, evaluate against truth z-scores, or generate candidate peptides unconditionally or from MMP z-score profiles.

Perform multiple sequence alignment on protein or nucleotide sequences using the Clustal Omega algorithm.

ColabDock is a protein-protein docking framework that uses AlphaFold2 to predict complex structures guided by experimental restraints from cross-linking mass spectrometry, NMR, or other sources.

Identify CpG islands in DNA sequences using the Gardiner-Garden and Frommer criteria. Analyze GC content, CpG density, and observed/expected ratios.

Convert CSV and TSV files containing sequence data to FASTA format with flexible column mapping and automatic delimiter detection

DeepEMhancer is a deep learning-based post-processing tool for cryo-EM maps. It performs automatic sharpening, masking, and denoising in a single step without requiring an atomic model. Supports half-map inputs for improved local mask estimation.

Predict peptide immunogenicity with DeepImmuno-CNN from peptide sequences and HLA alleles.

DFMDock (Denoising Force Matching Dock) is a diffusion model that unifies sampling and ranking for protein-protein docking within a single framework. It predicts docked poses for protein-protein complexes from unbound structures using denoising score matching with optional clash force guidance.

AI-powered antibody CDR design using equivariant diffusion models. Generates optimized complementarity-determining region (CDR) sequences and structures for antibodies targeting specific antigens. Supports single CDR, multi-CDR co-design, and fixed-backbone sequence design modes.

DiffDock-L is a state-of-the-art molecular docking tool that uses diffusion models to predict how small molecule ligands bind to protein targets. It generates multiple binding poses with confidence scores.

DLKcat predicts enzyme turnover numbers (kcat values) from protein sequences and substrate structures using deep learning. Combines CNN and GNN architectures for accurate kinetic parameter prediction.

Generate batches of mutated DNA variants from one or more FASTA sequences. Create substitution, insertion, deletion, or mixed variant libraries with reproducible settings.

Shuffle DNA sequences while preserving nucleotide, dinucleotide, or k-mer composition for generating randomized control sequences

Translate DNA sequences to protein sequences using genetic code

Convert DNA sequences to RNA (transcription) - replaces T with U

Generate random DNA sequences with customizable length, GC content, and restriction sites for molecular cloning and testing purposes.

Assess docking model quality by comparing predicted complexes against native references. DockQ v2.1.3 supports protein, nucleic-acid, and supported small-molecule interfaces with faithful upstream metrics.

DR-BERT is a compact protein language model that predicts intrinsically disordered regions (IDRs) in proteins. It outputs per-residue disorder probability scores (0–1) from amino acid sequences, enabling fast and accurate annotation of disordered regions without structural data.

Assign protein secondary structure using the DSSP algorithm. The gold standard for hydrogen bond-based structure assignment from coordinates.

DynamicBind is an AI-powered protein-ligand binding prediction tool that recovers ligand-induced conformational changes from unbound protein structures. It predicts both ligand binding poses and protein conformational changes.

EquiDock is an SE(3)-equivariant graph neural network for rigid protein-protein docking. It predicts a binding pose for a protein-protein complex from unbound structures using geometric deep learning, with DIPS and DB5 pretrained checkpoints from the upstream release.

ESM-2 is a 650M parameter protein language model from Meta AI trained on 250M protein sequences. Generate rich sequence representations for downstream tasks like structure prediction, function annotation, and variant effect prediction.

Inverse folding with ESM-IF1. Design protein sequences for given 3D backbone structures using a geometric deep learning model. Generate multiple sequence variants optimized for your target structure.

ESMfold is a fast, single-sequence protein structure predictor from Meta AI. Predicts 3D protein structures directly from amino acid sequences without requiring multiple sequence alignments (MSA), making it significantly faster than AlphaFold while automatically scaling GPU resources for larger proteins.

Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.

EvoDiff is a diffusion-based protein sequence generation framework from Microsoft Research. ProteinIQ currently wraps the EvoDiff-Seq OA_DM_38M model for unconditional protein generation, motif scaffolding, and user-sequence inpainting.

Optimize protein binders using genetic algorithms combined with AlphaFold2 fitness evaluation and ProteinMPNN sequence design. EvoPro evolves protein sequences to maximize binding affinity and structural quality through iterative cycles of mutation, selection, and validation.

Calculate the molar extinction coefficient of protein sequences at 280 nm. Used for protein concentration determination by UV spectroscopy.

Split large FASTA files into smaller chunks. Divide by sequence count or create individual files for each sequence.

Convert FASTA sequence files to FASTQ format with mock quality scores

Convert FASTQ sequence files to FASTA format

Infer approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences.

Clean and filter DNA sequences by removing or replacing non-standard nucleotide characters. Supports multiple filter modes including standard 4 bases, IUPAC ambiguity codes, and custom character sets.

Clean and filter protein sequences by removing or replacing non-standard amino acid characters. Supports multiple filter modes including standard 20 amino acids, IUPAC codes, and custom character sets.

Match experimental peptide masses against theoretical digest fragments of a protein sequence. Identify peptides from mass spectrometry data by peptide mass fingerprinting.

Fast protein structure search, comparison, and clustering. Search your structure against 200M+ AlphaFold predictions, compare 2 structures, or cluster up to 2500.

Open-source protein pocket detection using Voronoi tessellation and alpha spheres. Identifies ligand binding sites with druggability scores.

Calculate GC content and nucleotide composition of DNA/RNA sequences. Analyze individual sequences or get combined statistics.

Extract sequence features (CDS, mRNA, gene, etc.) from GenBank files in FASTA format with support for spliced features

Convert GenBank files to FASTA format

GenMol is a generative AI model from NVIDIA that creates novel drug-like molecules using masked discrete diffusion. It generates molecules in SAFE representation format and supports de novo generation, linker design, motif extension, and scaffold decoration.

Find potential N-linked glycosylation sites (NX[S/T] sequons) in protein sequences. Identifies asparagine residues in the consensus motif for N-glycosylation.

Calculate binding free energies using MM/PBSA and MM/GBSA methods for protein-ligand, protein-protein, and protein-DNA complexes. Provides detailed energy decomposition and per-residue contributions.

GNINA is a molecular docking tool that combines traditional physics-based docking with deep learning CNN scoring for protein-small-molecule complexes. It provides accurate binding predictions with confidence scores, optimized for high-throughput virtual screening.

Calculate the GRAVY (Grand Average of Hydropathy) score of protein sequences. Positive values indicate hydrophobic proteins, negative values indicate hydrophilic proteins.

Run molecular dynamics simulations using the GROMACS engine with classical force fields (AMBER, CHARMM, GROMOS, OPLS). Study protein dynamics, conformational flexibility, and structural stability with production-grade MD methodology.

HADDOCK (High Ambiguity Driven protein-protein DOCKing) is an integrative modeling platform for biomolecular complexes. It uses experimental data and bioinformatic predictions to guide the docking process, generating accurate protein-protein complex structures.

Cyclic peptide structure prediction using HighFold, a modified ColabFold/AlphaFold2 framework with CycPOEM (Cyclic Position Offset Encoding Matrix) for head-to-tail and disulfide bridge constraints.

Sensitive sequence homology search using profile hidden Markov models. More accurate than BLAST for detecting remote homologs, ideal for finding evolutionarily distant protein family members.

Humatch is an antibody humanization tool that transforms non-human antibody sequences into humanized variants. Uses three lightweight CNNs to identify optimal human V-genes and generate paired heavy and light chain sequences with minimal edits while maintaining functionality.

Generate Kyte-Doolittle hydropathy plots to visualize hydrophobic and hydrophilic regions along protein sequences. Identify transmembrane domains and surface-exposed regions.

Generate hydrophobicity plots using 24 different amino acid scales. Visualize hydrophobic and hydrophilic regions for protein analysis, epitope prediction, and membrane protein studies.

Design thermostable protein sequences using ProteinMPNN trained on hyperthermophilic organism structures. Generates sequences optimized for improved thermal stability without requiring ligands or additional context.

Analyze immunoglobulin (antibody) and T cell receptor variable domain sequences. Identifies V/D/J gene segments, delineates CDR regions, and analyzes rearrangement junctions.

Design antibody CDR sequences via inverse folding. Generates complementarity-determining region (CDR) sequences for antibodies targeting therapeutic antigens using deep learning. Optimizes CDR loops (HCDR1, HCDR2, HCDR3) based on antibody-antigen complex structures.

IgGM is a generative foundation model for antibody and nanobody design against a target antigen. Supports CDR design, affinity maturation, inverse design, and framework design. Requires an antigen structure (PDB) and antibody sequences with "X" marking positions to design.

ImmuneBuilder predicts 3D structures of immune receptor proteins including antibodies, nanobodies, and T-cell receptors. It uses ABodyBuilder2, NanoBodyBuilder2, and TCRBuilder2/TCRBuilder2+ to generate structures with per-residue error estimates and optional ensemble artifacts.

Convert InChI strings into SMILES with batch support and downloadable outputs.

Calculate the instability index of protein sequences. Values above 40 indicate an unstable protein with a short half-life in vitro.

Controllable biomolecular structure prediction model for proteins, ligands, DNA, RNA, and multi-component complexes. IntelliFold 2 supports fast v2-Flash inference, optional MSA generation, and ranked confidence outputs.

Isoelectric Point Calculator 2.0 - Predict protein/peptide isoelectric point (pI) using 18+ validated pKa scales, SVR models, and deep learning. Supports proteins, peptides, and comprehensive analysis.

Scoring function for interprotein interactions in AlphaFold2, AlphaFold3 and Boltz predictions. Calculates ipSAE, ipTM, pDockQ, pDockQ2, and LIS scores to assess protein-protein interface quality.

Build phylogenetic trees using maximum likelihood with automatic model selection (ModelFinder) and ultrafast bootstrap support.

Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery

Fix ligand files that fail RDKit, Meeko, or docking preparation. Repair SDF, MOL, and MOL2 inputs, apply safe chemistry cleanup, and export docking-ready SDF files.

Design protein sequences with atomic context from ligands, metals, and nucleotides. Achieves 63.3% sequence recovery at binding sites, significantly outperforming ProteinMPNN (50.5%).

LightDock is a protein-protein, protein-peptide, and protein-DNA docking framework using Glowworm Swarm Optimization (GSO). It predicts macromolecular binding modes and interfaces for biological complexes.

Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.

LMI4Boltz is a low-memory fork of Boltz for biomolecular structure and binding affinity prediction. It preserves Boltz inference behavior while reducing VRAM use with in-place pair updates, CPU offload, reduced precision pair representation, and aggressive chunking.

Perform multiple sequence alignment using MAFFT (Multiple Alignment using Fast Fourier Transform). Supports multiple algorithms from fast progressive to highly accurate iterative methods.

Design VHH nanobody binders using AlphaFold-Multimer with structure templates and sequence conditioning. mBER (Manifold Binder Engineering and Refinement) generates novel VHH antibody sequences that bind to user-specified target proteins.

Analyze molecular dynamics trajectories using a ProteinIQ wrapper pinned to MDAnalysis 2.9.0. Calculate RMSD, residue-aggregated RMSF, radius of gyration, distance tracking, and additional trajectory observables from standard topology and trajectory files.

MDGen is a generative AI model for molecular dynamics trajectory generation. Generate physically plausible conformational ensembles from a single protein structure, enabling rapid exploration of protein dynamics without expensive MD simulations.

MiniFold is a fast single-sequence protein structure predictor that is 10-20x faster than ESMFold. It predicts 3D protein structures directly from amino acid sequences without requiring multiple sequence alignments (MSA), making it ideal for rapid structure prediction.

Ultra-fast sequence search and clustering. 10,000x faster than BLAST for database searches, with powerful sequence clustering capabilities for proteins and nucleotides.

Convert MOL2 ligand files into SMILES strings for registration, filtering, and downstream analysis.

Compute 200+ RDKit molecular descriptors, drug-likeness rule violations, and structural fingerprints for QSAR, virtual screening, and ML workflows

Validate protein structure quality with all-atom contact analysis, Ramachandran plots, rotamer assessment, and geometry checks.

Interactive viewer for multiple sequence alignments with color-coded residues and consensus sequence

Rapidly align and compare DNA sequences using MUMmer4 nucmer. Perform pairwise genome comparisons to identify SNPs, indels, and structural variants between reference and query genomes.

Perform multiple sequence alignment using MUSCLE5 (MUltiple Sequence Comparison by Log-Expectation). Uses the PPP algorithm for high-quality alignments with support for ensemble generation.

Predict protein solubility and usability for E. coli expression using ESM protein language models

All-atom generative AI for designing protein binders. Specify target binding sites and generate diverse binding proteins with fine-grained control over interaction parameters.

Convert single-letter amino acid codes to three-letter codes

Run alchemical free energy calculations for drug discovery using Open Free Energy. Supports Absolute Hydration Free Energy (AHFE) and Relative Binding Free Energy (RBFE) calculations with GPU-accelerated OpenMM simulations.

OpenFold-3 is an open-source AI model for biomolecular structure prediction, aiming to reproduce AlphaFold3. Predicts 3D structures for proteins, RNA, DNA, and small molecule ligands with high accuracy.

Run GPU-accelerated molecular dynamics simulations using OpenMM. Simulate protein dynamics, study conformational changes, and analyze stability with industry-standard force fields (AMBER, CHARMM).

ORB v3 is a universal interatomic potential (machine learning force field) that predicts energies, forces, and stress tensors for atomic systems. Supports both molecular and materials structures with geometry optimization using conservative and direct model variants.

Find all Open Reading Frames (ORFs) in DNA sequences. Searches all six reading frames and supports multiple genetic codes.

Screen compounds for Pan-Assay INterference patterns that cause false positives in biological assays

Open-source molecular docking platform using physics-based scoring functions. CPU-optimized algorithms achieve sub-angstrom accuracy (0.014A RMSD) without GPU requirements.

ParaSurf is a state-of-the-art surface-based deep learning model for predicting interactions between antibodies and antigens. It identifies paratope binding sites on antibody structures with high accuracy across multiple benchmark datasets.

Convert Protein Data Bank files to Crystallographic Information File format

Convert Protein Data Bank files to FASTA sequence format

Convert Protein Data Bank files to MOL2 molecular format

Convert Protein Data Bank files to Structure Data Format

Visualize and analyze protein structures in 3D using Mol* (molstar), the same viewer used by AlphaFold DB and RCSB PDB

PDB2PQR prepares protein structures for electrostatics calculations by adding missing atoms, predicting protonation states using PROPKA, and assigning atomic charges and radii from standard force fields.

PDBFixer is an OpenMM-based tool used for fixing problems in protein/DNA/RNA structure files, including adding missing atoms, adding missing residues, and fixing improper formatting.

Design linear peptide binders for target proteins using a target sequence-conditioned masked language model. PepMLM generates peptide sequences optimized to bind specific protein targets based on ESM-2 protein language modeling.

Predict protease and chemical cleavage sites across a protein sequence for up to 39 enzymes simultaneously. Identify where each enzyme cuts, the cleavage residue, and context window around each site.

Cleave a protein sequence with a chosen protease and compute the masses of the resulting peptides. Supports multiple enzymes, missed cleavages, chemical modifications, and different ion types for mass spectrometry experiment planning.

Calculate the theoretical isoelectric point (pI) of protein sequences. The pI is the pH at which a protein carries no net electrical charge.

PocketFlow is a structure-based molecular generative model that designs novel drug-like molecules within protein binding pockets. It uses autoregressive flow modeling with chemical knowledge to generate 100% chemically valid, highly drug-like compounds.

PoseBusters validates generated or docked molecular poses with chemically and structurally grounded quality checks for molecular geometry, intermolecular interactions, and optional reference-pose agreement.

PPAP (Protein-Protein Affinity Predictor) predicts binding affinity (ΔG and Kd) between interacting protein chains using deep learning with ESM2-3B embeddings. Requires a PDB with 2+ protein chains. Note: This tool is for protein-protein interactions only, not protein-ligand binding.

Design PCR primers for DNA sequences with upstream-faithful Primer3 controls for target regions, thermodynamics, product size, and primer quality.

ProFam-1 is a protein family language model for family-conditioned sequence generation. Provide a protein family FASTA/MSA and generate new sequences with model likelihood scores for downstream ranking and screening.

ProGen2 is Salesforce Research's protein language model suite for prompt-based de novo protein sequence generation. It samples novel amino acid sequences from a plain-text context string using top-p sampling and temperature control.

Predict pKa values of ionizable groups in proteins and protein-ligand complexes from 3D structure. PROPKA calculates environment-driven pKa shifts for standard ionizable residues, terminal groups, and supported ligand atom types.

ProstT5 is a protein language model that bidirectionally translates between amino acid sequences and 3Di structural tokens. It enables fast structure-based searches and inverse folding by encoding structural information into a sequence-like representation.

Plot net charge vs pH for protein sequences. Visualize how protein charge changes across pH 0-14 and identify the isoelectric point (pI) where the net charge crosses zero.

Calculate the molecular weight (MW) of protein sequences in Daltons. Supports FASTA format input and batch processing.

Scan protein sequences for biologically important motifs including glycosylation sites, phosphorylation sites, nuclear localization signals, prenylation motifs, and more.

Calculate protein parameters, including molecular weight, theoretical pI, extinction coefficients, aromaticity, secondary structure fractions, atomic composition, estimated half-life, and several indices, including instability, aliphatic index, and GRAVY.

Generate amino acid property profiles using 42 different scales spanning hydrophobicity, secondary structure propensity, flexibility, polarity, surface accessibility, antigenicity, and more.

Predict protein stability using validated BioPython methods: Instability Index, Aliphatic Index, GRAVY, flexibility analysis, and charge distribution

Reverse translate protein sequences to possible DNA sequences

Design protein sequences for given backbone structures using deep learning. Fast and accurate inverse folding with state-of-the-art sequence recovery (52.4%).

Open-source AlphaFold 3 implementation by ByteDance for biomolecular structure prediction. Predicts 3D structures for proteins, RNA, DNA, and small molecule ligands with high accuracy.

Generate random protein sequences with customizable length, composition, and amino acid properties

Statistical Coupling Analysis for protein families. Identifies co-evolving residue groups (sectors) from multiple sequence alignments using the SCA method from the Ranganathan Lab.

Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.

Calculate the radius of gyration (Rg) for protein structures from PDB files. Supports multiple chains and atom selection options.

Generate Ramachandran plots from PDB structures to analyze protein backbone dihedral angles (phi/psi). Visualize favored, allowed, and outlier regions.

Perform maximum-likelihood phylogenetic tree inference with RAxML-NG for aligned protein or DNA sequences. Supports ML search, bootstrap analysis, and upstream-style automatic model-family selection.

Generate reverse, complement, or reverse-complement of DNA/RNA sequences

Structure-based de novo antibody and nanobody design pipeline combining antibody-tuned RFdiffusion, ProteinMPNN sequence design, and antibody-tuned RoseTTAFold2 filtering.

RFdiffusion is a state-of-the-art protein structure generation tool that uses diffusion models to design proteins de novo, create binders, scaffold motifs, and generate symmetric oligomers with atomic precision.

RFdiffusion2 is an atom-level enzyme active site scaffolding tool that generates protein scaffolds around your input motif. REQUIRES an input PDB structure containing the active site residues to scaffold. For ligand-aware design, ligands must be embedded in the input PDB as HETATM records.

All-atom generative diffusion model for protein design with complex constraints. Design binders, enzymes, and symmetric protein assemblies.

Calculate Root Mean Square Deviation (RMSD) between protein structures. Compare a reference PDB against multiple structures with automatic Kabsch alignment.

Convert RNA sequences to DNA (reverse transcription) - replaces U with T

RNAalifold computes consensus RNA secondary structure from a multiple sequence alignment. Uses covariation information to improve prediction accuracy for evolutionarily conserved structures.

RNAcofold predicts the joint secondary structure of two interacting RNA molecules and optionally reports partition-function and concentration-dependent equilibrium metrics.

RNAdistance compares RNA secondary structures using the selected upstream ViennaRNA distance representation and comparison mode.

RNAdos calculates density-of-states summaries for RNA sequences, reporting representative structures and state counts across energy bands.

RNAduplex computes the hybridization structure between two RNA sequences. Predicts the optimal duplex formation and binding energy.

RNAeval calculates the free energy of an RNA secondary structure for a given sequence. Evaluates if a proposed structure is thermodynamically favorable.

RNAfold predicts RNA secondary structure using minimum free energy (MFE) algorithms and optionally returns partition-function ensemble metrics when explicitly enabled.

Generate random RNA sequences with customizable types and structural features

RNAinverse designs RNA sequences for a specified target secondary structure using ViennaRNA inverse-folding semantics.

RNALfold reports locally stable RNA secondary structures within a sliding window and returns their start and end positions on the input sequence.

RNAplex predicts fast query-target RNA interactions, reporting parsed hit coordinates, structures, and energies.

RNAplfold computes local base pair probabilities using a sliding window approach. Useful for analyzing accessibility and identifying binding sites in long RNA sequences.

RNAplot renders ViennaRNA secondary-structure plot files from a supplied RNA sequence and dot-bracket structure.

RNAsubopt enumerates all RNA secondary structures within a specified energy range above the minimum free energy (MFE). Useful for exploring the structural ensemble and identifying alternative conformations.

RNAup predicts accessibility-aware RNA-RNA interactions, reporting opening-energy terms alongside interaction energies and downloadable upstream output files.

Open-source structure prediction neural network for proteins, nucleic acids, and small molecules. State-of-the-art accuracy with multi-chain support.

Quantify transcript abundance from RNA-seq reads with Salmon selective alignment. Upload a transcript FASTA reference plus single-end or paired-end FASTA/FASTQ reads to produce TPM and estimated read-count tables.

Calculate Solvent Accessible Surface Area (SASA) for protein structures using the Shrake-Rupley algorithm.

Geometric deep learning model for predicting protein binding sites directly from 3D structure. Identifies where proteins interact with other proteins, antibodies, or disordered proteins with high accuracy, including for novel protein folds.

Convert Structure Data Format files to Protein Data Bank format

Convert SDF ligand files, including multi-record batches, into SMILES strings.

SigmaDock is a fragment-based molecular docking tool using SE(3) equivariant diffusion models to predict how small molecule ligands bind to protein targets. Presented at ICLR 2026, it generates multiple binding poses with Vinardo scoring.

Convert SMILES strings into InChI strings with batch support and downloadable outputs.

Convert SMILES strings into 3D MOL2 files for docking and molecular modeling workflows.

Convert SMILES strings into 3D PDB files for molecular visualization and downstream docking preparation.

Convert single SMILES strings or small batches into 3D SDF files with downloadable per-entry files and a combined batch output.

SMINA is a fork of AutoDock Vina with enhanced scoring functions, custom scoring support, and 10-20x faster minimization. Ideal for scoring function development, pose refinement, and high-performance docking workflows.

Deep learning framework for predicting small molecule-RNA interactions using RNA secondary structure. Combines language models, CNNs, and graph attention networks for binding prediction.

Specialized model for soluble protein sequence design. Trained exclusively on soluble proteins for optimized performance on cytoplasmic and extracellular proteins.

SPRINT (Structure-aware PRotein ligand INTeraction) predicts drug-target interactions using co-embedded protein and ligand representations. Screen thousands of compounds against a protein target in seconds.

Predict protein hydration sites from a structure using a diffusion model with ESM features and a confidence-filtering head.

SurfDock is a surface-informed diffusion generative model for protein-ligand docking, published in Nature Methods 2024. It leverages protein surface geometry to guide a diffusion process for reliable and accurate protein-ligand complex prediction.

Predict protein thermostability changes (ΔΔG) for point mutations using a graph neural network. Enables computational saturation mutagenesis screening to identify stabilizing mutations.

Convert three-letter amino acid codes to single-letter codes

Predict MHC class II peptide immunogenicity (CD4+ T cell response) using transfer learning with LSTM.

Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.

Convert plain text sequences to FASTA format - supports DNA, RNA, and protein sequences with automatic cleanup and validation

USAlign (Universal Structure Alignment) aligns protein, RNA, and DNA structures to compute TM-scores and generate superposed structures. Compare 3D structures to assess structural similarity.

Veber's Rule predicts oral bioavailability by evaluating molecular weight, LogP, hydrogen bond donors/acceptors, and rotatable bonds

ViennaRNA exposes a curated set of upstream-faithful ViennaRNA 2.7.2 workflows for RNA folding, density-of-states analysis, interaction prediction, local accessibility, plotting, inverse folding, and structure analysis.