Results for “molecular-machine-learning”
21 skillsMore results
levalencia molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
3 · bundle
artubss molfeat
Featurização molecular para ML (100+ featurizadores). ECFP, MACCS, descritores, modelos pré-treinados (ChemBERTa), converter SMILES em features, para QSAR e ML molecular.
10 · bundle
jackychenlu molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
0 · bundle
metinduraktr-44 molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
0 · bundle
bouclem deep-learning
PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production
7 · bundle
chen-yu-hao molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
5 · bundle
jiachen-t-wang multimodal-learning-with-transformers-a-survey-arxiv-2206-06
Multimodal Learning with Transformers: A Survey
6
k-dense-ai molecular-dynamics
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).
30.2k · bundle
jiachen-t-wang matryoshka-representation-learning-arxiv-2205-13147v4
Matryoshka Representation Learning
6
jarbitechture learn
Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.
0 · bundle
jiachen-t-wang multimodal-few-shot-learning-with-frozen-language-models-arx
Multimodal Few-Shot Learning with Frozen Language Models
6
jiachen-t-wang hard-negative-mixing-for-contrastive-learning-arxiv-2010-010
Hard Negative Mixing for Contrastive Learning
6
alterlab-ieu alterlab-matchms
Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use pyopenms. Part of the AlterLab Academic Skills suite.
60 · bundle
levalencia matchms
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
3 · bundle
jiachen-t-wang lora-low-rank-adaptation-of-large-language-models-arxiv-2106
LoRA: Low-Rank Adaptation of Large Language Models
6
jiachen-t-wang training-compute-optimal-large-language-models-arxiv-2203-15
Training Compute-Optimal Large Language Models
6
jiachen-t-wang autoaugment-learning-augmentation-strategies-from-data-arxiv
AutoAugment: Learning Augmentation Strategies from Data
6
jiachen-t-wang multimodal-neurons-in-artificial-neural-networks-arxiv-2103-
Multimodal Neurons in Artificial Neural Networks
6
jiachen-t-wang el2n-deep-learning-on-a-data-diet-arxiv-2107-07075v2
EL2N: Deep Learning on a Data Diet
6
k-dense-ai molfeat
Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.
30.2k · bundle