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Prediction and Designing of thermostable proteins with a desired melting temperature

INDUS-Retriever-small (previously nasa-smd-ibm-st.38m) is

protein sequence design in the context of small-molecule ligands

A system for rapidly aligning entire genomes, whether in complete

LLM for scientific research papers

energetics (energy and entropy contributions) around proteins and small molecules.

Accurate prediction of drug-target binding affinity is essential in

Drugs must satisfy stringent criteria for both efficacy and safety

Drugs must satisfy stringent criteria for both efficacy and safety

Drugs targeting the central nervous system must meet stringent criteria

Accurate prediction of drug-target binding affinity is essential in

proteins, small molecules, and single-cell gene data. Designed for robust

protein phenotypes, which combines a large language model with protein

LLM agents across scientific domains

Boltz-1:

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-HIV-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-FREESOLV-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-QM7-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BBBP-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-ESOL-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-CLINTOX-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-TOXCAST-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-LIPOPHILICITY-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-TOX21-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-SIDER-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-MUV-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model

Large-scale biomolecular instruction dataset for chemistry/biology LLMs (ICLR2024)

SKESA is a de-novo sequence read assembler for microbial

Indus-Retriever (nasa-smd-ibm-st-v2) is a Bi

A molecular informatics toolkit with an integration of bioinformatics and

protein). HERON also calculates significance on the probe, epitope, and protein

protein sequence design (inverse folding) from backbone structures, achieving 52.4% sequence

protein drug targets, fine-tuned from gbyuvd/chemselfies-base-bertmlm on a dataset

proteins in LC-MS/MS proteomics data. This test detects

Resources on ChIP-seq data which include papers, methods, links

UNIX-style FASTA manipulation tools.

An ontology of processes triggered by homeostatic imbalance, with a

protein structure design with multi-motif scaffolding capabilities, achieving state

Protein Modeling DTI, Drug Property, PPI, DDI, Protein Function Prediction

This model is deprecated. please use the updated sentence transformer

The 'funOmics' package ggregates or summarizes omics data into higher

protein-protein interactions as priors for gene co-expression, netsmooth

A collection of research papers for AI-based protein design.

proteins at either bulk level or cell type level can be integrated

tidyFlowCore bridges the gap between flow cytometry analysis using the

Generative pre-training for genomics