| eos3804 | chemprop-abaumannii | Inhibition of Acinetobacter baumannii growth | 1 column | This model is a Chemprop neural network trained with a growth inhibition dataset. Authors screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. They discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. | https://github.com/ersilia-os/eos3804 |
| eos42ez | antibiotics-ai-cytotox | Human cytotoxicity endpoints | 3 columns | The authors tested the dataset of 39312 compounds used to train the antibiotics-ai model (eos18ie) against several cytotoxicity endpoints; human liver carcinoma cells (HepG2), human primary skeletal muscle cells (HSkMCs) and human lung fibroblast cells (IMR-90). Cellular viability was measured after 20133 days of treatment with each compound at 10 μM and activities were binarized using a 90% cell viability cut-off. 341 (8.5%), 490 (3.8%) and 447 (8.8%) compounds classified as cytotoxic for HepG2 cells, HSk-MCs and IMR-90 cells | https://github.com/ersilia-os/eos42ez |
| eos2db3 | chemical-space-projections-chemdiv | Chemical space 2D projections against ChemDiv | 8 columns | This tool performs PCA, UMAP and tSNE projections taking a 100k ChemDiv diversity set as a chemical space of reference. The Ersilia Compound Embeddings are used as descriptors. Four PCA components and two UMAP and tSNE components are returned. | https://github.com/ersilia-os/eos2db3 |
| eos18ie | antibiotics-ai-saureus | Antibiotic activity prediction against Staphylococcus aureus | 1 column | The authors use a mid-size dataset (more than 30k compounds) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci. | https://github.com/ersilia-os/eos18ie |
| eos37l0 | chembl-kpneumoniae | Klebsiella pneumoniae activity prediction | 22 columns | Klebsiella pneumoniae activity prediction based on phenotypic ChEMBL data. Each column corresponds to a specific bioactivity dataset derived from ChEMBL, encompassing multiple assays and binarization cut-offs. The global consensus score summarizes the probability of being active. Model developed by Ersilia. | https://github.com/ersilia-os/eos37l0 |
| eos5dti | chembl-abaumannii | Acinetobacter baumannii activity prediction | 26 columns | Acinetobacter baumannii activity prediction based on phenotypic ChEMBL data. Each column corresponds to a specific bioactivity dataset derived from ChEMBL, encompassing multiple assays and binarization cut-offs. The global consensus score summarizes the probability of being active. Model developed by Ersilia. | https://github.com/ersilia-os/eos5dti |
| eos2m0f | chembl-saureus | Staphylococcus aureus activity prediction | 51 columns | Staphylococcus aureus activity prediction based on phenotypic ChEMBL data. Each column corresponds to a specific bioactivity dataset derived from ChEMBL, encompassing multiple assays and binarization cut-offs. The global consensus score summarizes the probability of being active. Model developed by Ersilia. | https://github.com/ersilia-os/eos2m0f |
| eos7m30 | admet-ai-exact | ADMET properties prediction | 49 columns | ADMET AI is a framework for carrying out fast batch predictions for ADMET properties. It is based on ensemble of five Chemprop-RDKit models and has been trained on 41 tasks from the ADMET group in Therapeutics Data Commons (v0.4.1). Out of these 41 tasks, there are 31 classification tasks and 10 regression tasks. In addition to that output also contains 8 physicochemical properties, namely, molecular weight, logP, hydrogen bond acceptors, hydrogen bond doners, Lipinskis Rule of 5, QED, stereo centers, and topological polar surface area. eos7d58 contains an implementation of the model that also produces the percentile based on DrugBank approved drugs. | https://github.com/ersilia-os/eos7m30 |
| eos9ei3 | sa-score | Synthetic accessibility score | 1 column | Estimation of synthetic accessibility score (SAScore) of drug-like molecules based on molecular complexity and fragment contributions. The fragment contributions are based on a 1M sample from PubChem and the molecular complexity is based on the presence/absence of non-standard structural features. It has been validated comparing the SAScore and the estimates of medicinal chemist experts for 40 molecules (r2 = 0.89). The SAScore has been contributed to the RDKit Package. | |
| eos9yui | natural-product-likeness | Natural product likeness score | 1 column | The model is a derivation of the natural product fingerprint (eos6tg8). In addition to generating specific natural product fingerprints, the activation value of the neuron that predicts if a molecule is a natural product or not can be used as a NP-likeness score. The method outperforms the NP_Score implemented in RDKit. | |