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# MMedS-Llama3 💻Github Repo 🖨️arXiv Paper

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…

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.

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.

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.

Drugs targeting the central nervous system must meet stringent criteria for both efficacy and safety, including their ability to penetrate the blood-brain barrier (BBB). This model predicts the likelihood of small-molecule drugs crossing the BBB, a critical factor in CNS drug development.

Accurate prediction of drug-target binding affinity is essential in the early stages of drug discovery. Traditionally, binding affinities are measured through high-throughput screening experiments, which, while accurate, are resource-intensive and limited in their scalability to evaluate large sets…

The ibm/biomed.omics.bl.sm.ma-ted-458m model is a biomedical foundation model trained on over 2 billion biological samples across multiple modalities, including proteins, small molecules, and single-cell gene data. Designed for robust performance, it achieves state-of-the-art results over a variety…

ProCyon-Full is a multimodal foundation model for protein phenotypes, which combines a large language model with protein encoders to support inputs of interleaved free text and proteins. This model is instruction-tuned using the full ProCyon-Instruct dataset.

Boltz-1:

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-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…

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-FREESOLV-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-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…

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BBBP-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-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-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…

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-TOXCAST-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-LIPOPHILICITY-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,…

# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-TOX21-101 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-SIDER-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-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…

Dataset for paper "Teach Multimodal LLMs to Comprehend Electrocardiographic Images".

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

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.

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…

The Mistral-DNA-v1-138M-bacteria Large Language Model (LLM) is a pretrained generative DNA text model with 17.31M parameters x 8 experts = 138.5M parameters. It is derived from Mistral-7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced.

This is a ReactionT5 pre-trained to predict the products of reactions.

Universal Cell Embeddings (UCE) is a foundation model designed for single-cell RNA sequencing data analysis. UCE generates a universal representation of cells that captures the molecular diversity across different cell types, tissues, and species.

# JSL-MedLlama-3-8B-v2.0

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

Abstract:

Abstract:

SMILES2IUPAC-canonical-base was designed to accurately translate SMILES chemical names to IUPAC standards.

### Model Description A machine learning model for waste classification

This model was finetuned on concatenated pairs of interacting proteins in much the same way as PepMLM. It is meant to generate interaction partners for proteins using the masked language modeling capabilities of ESM-2. The model is not well tested, so use with caution.

ProstT5 is a protein language model (pLM) which can translate between protein sequence and structure. !ProstT5 pre-training and inference

Question Answering Model for the PathoTHREAT Project

Pre-trained weights and exported models for our spine segmentation project. The source code, designed to reproduce our test results and facilitate training and running inference on your own data, is available on GitHub: https://github.com/MMIV-ML/fastMONAI/tree/master/research

This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).

I present a demo showcasing retinal vessel segmentation using the U-Net model, which is a well-known and widely used model in medical image segmentation. The model was trained on the DRIVE dataset, and the training process was conducted on Google Colab.

datasets: - UMLS

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.