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This model is a fine-tuned version of DeBERTa on the PubMED Dataset.

BiomedCLIP is a biomedical vision-language foundation model that is pretrained on PMC-15M, a dataset of 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central, using contrastive learning.

Deep learning for chemistry and materials science remains a novel field with lots of potiential. However, the popularity of transfer learning based methods in areas such as NLP and computer vision have not yet been effectively developed in computational chemistry + machine learning.

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

This model is a BERT-like sequence classifier for 221 human protein drug targets, fine-tuned from gbyuvd/chemselfies-base-bertmlm on a dataset derived ChemBL34 (Zdrazil et al. 2023). It predicts potential drug targets using chemical structures represented as SELFIES (Self-Referencing Embedded…

T-cell receptor (TCR) binding to immunogenic peptides (epitopes) presented by major histocompatibility complex (MHC) molecules is a critical mechanism in the adaptive immune system, essential for antigen recognition and triggering immune responses.

In search engines, rerankers are crucial for improving the accuracy of your retrieval system.

In retrieval systems, embedding models determine the quality of your search.

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-MUV-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model for small molecules created using MMELON (Multi-view Molecular Embedding with Late Fusion), a flexible approach to aggregate multiple views (sequence, image, graph) of…

Drugs must satisfy stringent criteria for both efficacy and safety. This model predicts the likelihood of FDA approval for small-molecule drugs, represented using SMILES (Simplified Molecular Input Line Entry System) strings.

Protein solubility is a critical factor in both pharmaceutical research and production processes, as it can significantly impact the quality and function of a protein. This is an example for finetuning ibm/biomed.omics.bl.sm-ted-458m for protein solubility prediction (binary classification) based…

DISCO (DIffusion for Sequence-structure CO-design) is a multimodal generative model that simultaneously co-designs protein sequences and 3D structures, conditioned on and co-folded with arbitrary biomolecules — including small-molecule ligands, DNA, and RNA.

Dans-PersonalityEngine-V1.2.0-24b ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠄⠀⡂⠀⠁⡄⢀⠁⢀⣈⡄⠌⠐⠠⠤⠄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀ ⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀ ⠀⢌⠀⠤⠀⢠⣞⣾⡗⠁⠀⠈⠁⢨⡼⠀⠀⠀⢀⠀⣀⡤⣄⠄⠈⢻⡇⠀⠐⣠⠜⠑⠁⠀⣀⡔⡿⠨⡄…

## Description: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology.

Drugs must satisfy stringent criteria for both efficacy and safety. This model predicts the likelihood of failure in clinical toxicity trials for small-molecule drugs, represented using SMILES (Simplified Molecular Input Line Entry System) strings.

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BACE-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model for small molecules created using MMELON (Multi-view Molecular Embedding with Late Fusion), a flexible approach to aggregate multiple views (sequence, image, graph) of…

ChemFIE-SA is a BERT-like sequence classifier for predicting synthesis accessibility given a SELFIES string of a compound, fine-tuned from gbyuvd/chemselfies-base-bertmlm on DeepSA's expanded dataset from Wang et al. 2023.

### Model Overview TabPFN-3 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/TabPFN. More details can be found in the Model Report.

# ibm-research/biomed.sm.mv-te-84m biomed.sm.mv-te-84m is a multimodal biomedical foundation model for small molecules created using MMELON (Multi-view Molecular Embedding with Late Fusion), a flexible approach to aggregate multiple views (sequence, image, graph) of molecules in a foundation model…

### Model Overview TabPFN-2.6 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN.

Dans-PersonalityEngine-V1.3.0-24b Dans-PersonalityEngine-V1.3.0-24b ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠄⠀⡂⠀⠁⡄⢀⠁⢀⣈⡄⠌⠐⠠⠤⠄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀ ⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀…

Welcome to the repository for Nidum-Limitless-Gemma-2B-GGUF, an advanced language model that provides unrestricted and versatile responses across a wide range of topics. This version is designed for maximum flexibility, allowing you to run it on both CPU and GPU.

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-ESOL-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model for small molecules created using MMELON (Multi-view Molecular Embedding with Late Fusion), a flexible approach to aggregate multiple views (sequence, image, graph) of…

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-HIV-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model for small molecules created using MMELON (Multi-view Molecular Embedding with Late Fusion), a flexible approach to aggregate multiple views (sequence, image, graph) of…

# InstaNovo: De novo Peptide Sequencing Model ## Model Description

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A domain-optimized reasoning model built on DeepSeek-R1-Distill-Qwen-32B, refined through a multi-stage pipeline of GPTQ quantization-aware training and QLoRA fine-tuning. Achieves 84% on MedQA — within 4 points of GPT-4o — in a ~20GB package that fits on a single L40/L40s GPU.

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-CLINTOX-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model for small molecules created using MMELON (Multi-view Molecular Embedding with Late Fusion), a flexible approach to aggregate multiple views (sequence, image, graph) of…

HuatuoGPT-o1-7B

Accurate prediction of drug-target binding affinity is essential in the early stages of drug discovery. This is an example of finetuning ibm/biomed.omics.bl.sm-ted-400 the task. Prediction of binding affinities using pKd, the negative logarithm of the dissociation constant, which reflects the…

Fine-tuned version of google/gemma-4-E4B-it across three professional domains — Medical, Legal, and Finance — using QLoRA (4-bit NF4) with Optuna-tuned hyperparameters, trained on Kaggle T4 GPU.

# ChemGPT 19M ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-QM7-101 biomed.sm.mv-te-84m is a multimodal biomedical foundation model for small molecules created using MMELON (Multi-view Molecular Embedding with Late Fusion), a flexible approach to aggregate multiple views (sequence, image, graph) of…

datasets: - UMLS

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

## Description: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology.

# Geneformer Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 54% better perplexity than standard knowledge distillation at 9.4x compression.