Model: Saideepthi55/sentencetransformer_ftmodel_on_chemical_dataset
Author: Saideepthi55
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, mpnet, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:117502, loss:MultipleNegativesRankingLoss, en, dataset:SwastikN/sxc_med_llm_chemical_gen, arxiv:1908.10084, arxiv:1705.00652, base_model:microsoft/mpnet-base, base_model:finetune:microsoft/mpnet-base, license:apache-2.0, model-index, endpoints_compatible, region:us
Base models (finetune): id: microsoft/mpnet-base
Card metadata: base_model: microsoft/mpnet-base
datasets: SwastikN/sxc_med_llm_chemical_gen
language: en
library_name: sentence-transformers
license: apache-2.0
metrics: cosine_accuracy, dot_accuracy, manhattan_accuracy, euclidean_accuracy, max_accuracy
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:117502, loss:MultipleNegativesRankingLoss
widget: source_sentence: Help me make the molecule CC(=O)OC[C@H](OC(C)=O)C(=O)N1CCCC[C@H]1C1CCN(C(=O)c2cc3ccccc3n2C)CC1 with the same hydrogen bond donors. The output molecule should be similar to the input molecule. Please inform me of the number of hydrogen bond donor(s) of the optimized molecule.
sentences: Your requirements guided the optimization, resulting in the molecule "CC(=O)OC(CCl)C(Cc1cccs1)[C@H](OC(C)=O)C(=O)N1CCCC[C@H]1C1CCN(C(=O)c2cc3ccccc3n2C)CC1" with an approximate hydrogen bond donor(s) of 0., Given a molecule expressed in SMILES string, help me optimize it according to my requirements., Help me adapt a molecular structure denoted in SMILES string based on my preferences., source_sentence: How can we modify the molecule CCC(CC)=C(CC)c1ccccc1OC(=O)OC(N=[N+]=[N-])c1ccccc1 to decrease its blood-brain barrier penetration (BBBP) value while keeping it similar to the input molecule? Please inform me of the BBBP value of the optimized molecule.
sentences: Describe a technology used for measuring people's emotional responses., I've successfully optimized the molecule according to your needs, resulting in "CCOC(=O)c1ccccc1OC(=O)OC(N=[N+]=[N-])c1ccccc1" with an approximate BBBP value of 0.71., Given a molecule expressed in SMILES string, help me optimize it according to my requirements., source_sentence: How can we modify the molecule C/C(=C/C(=O)N1CC[C@H](CC(CCCCCC(CO)C(=O)O)NC(=O)OC(C)(C)C)[C@H]1c1cccnc1)C(=O)O to increase its blood-brain barrier penetration (BBBP) value while keeping it similar to the input molecule?
sentences: Given a molecule expressed in SMILES string, help me optimize it according to my requirements., Aid me in refining a molecular structure written in SMILES notation based on my criteria., Taking your requirements into account, I've optimized the molecule to "C/C(=C/C(=O)N1CC[C@H](CNC(=O)[C@H](CO)NC(=O)OC(C)(C)C)[C@H]1c1cccnc1)C(=O)O"., source_sentence: Support me in transforming the molecule [SMILES] by incorporating the same hydrogen bond acceptors and maintaining its resemblance to the original molecule.
sentences: Taking your requirements into account, I've optimized the molecule to "CCOc1cccc(C2c3c(oc4ccc(C)cc4c3=O)C(=O)N2CCN(CC)CC)c1"., Help me adapt a molecular structure denoted in SMILES string based on my preferences., Help me adapt a molecular structure denoted in SMILES string based on my preferences., source_sentence: With a molecule represented by the SMILES string CNNNCC(=O)N[C@H](C)C[C@@H](C)NCc1ccc2c(c1)CCC2, propose adjustments that can increase its logP value while keeping the output molecule structurally related to the input molecule.
sentences: Aid me in refining a molecular structure written in SMILES notation based on my criteria., Given a molecule expressed in SMILES string, help me optimize it according to my requirements., In line with your criteria, I've optimized the molecule and present it as "C[C@H](C[C@@H](C)NC(=O)COC(C)(C)C)NCc1ccc2c(c1)CCC2".
model-index: name: MPNet base trained on sxc_med_llm_chemical_gen
results: task: type: triplet
name: Triplet
dataset: name: sxc med llm chemical gen
type: sxc_med_llm_chemical_gen
metrics: type: cosine_accuracy
value: 0.9837777777777778
name: Cosine Accuracy
verified: no, type: dot_accuracy
value: 0.01622222222222222
name: Dot Accuracy
verified: no, type: manhattan_accuracy
value: 0.9826666666666667
name: Manhattan Accuracy
verified: no, type: euclidean_accuracy
value: 0.9835555555555555
name: Euclidean Accuracy
verified: no, type: max_accuracy
value: 0.9837777777777778
name: Max Accuracy
verified: no
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Created: 2024-10-11 07:02:49