Predicting Small Molecule Potency to Inhibit Estrogen Receptors using Machine Learning and Deep Learning Approaches
Bibliographic record
Abstract
Uncovering new therapeutic potentials of existing approved drugs is an accelerated process of drug discovery as compared to designing a new drug from scratch. Launching a new drug into the market is challenging in terms of laborious efforts, time, cost and risks attached. Identifying the potency of the drug in terms of their binding affinity offers a new avenue of research concerning faster and cheaper health-care solution. In this regards, predicting the binding affinity of various drugs to a specific target receptor-like Estrogen receptor can lead to deeper insights. Estrogen receptor plays a significant role in diseases like breast cancer, ovarian cancer and endometrial cancer. Computational techniques like advanced deep learning models have shown effective results with complex data. In the proposed model, we construct a deep neural network with open-source Tensorflow python package to predict the binding affinities of small molecules with respect to Estrogen receptors. Small molecules are represented as feature vectors comprising of binding affinities to the other targets within the dataset. Based on the behaviour of a compound to inhibit the rest of the targets, we predict its potential binding affinity to bind to the estrogen receptors. The linear regression model is trained on the binding data from BindingDB database consisting of 7962 small molecules and a unique set of 995 target receptors. The performance of simple linear regression technique is compared to the deep neural network based linear regression estimator function in terms of mean squared error estimates. Better performance obtained with deep learning approach indicates that these advanced techniques in the domain of artificial intelligence should be further investigated for drug-target binding affinity prediction. Sources to reproduce the analysis are available at https://github.com/hetalraj/Bindingaffinity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".