<tt>OptiMol</tt> : Optimization of Binding Affinities in Chemical Space for Drug Discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ligand-based drug design has recently benefited from the development of deep generative models. These models enable extensive explorations of the chemical space and provide a platform for molecular optimization. However, the vast majority of current methods does not leverage the structure of the binding target, which potentiates the binding of small molecules and plays a key role in the interaction. We propose an optimization pipeline that leverages complementary structure-based and ligand-based methods. Instead of performing docking on a fixed chemical library, we iteratively select promising compounds in the full chemical space using a ligand-centered generative model. Molecular docking is then used as an oracle to guide compound optimization. This allows for iterative generation of compounds that fit the target structure better and better, without prior knowledge about bioactives. For this purpose, we introduce a new graph to Selfies Variational Autoencoder (VAE) which benefits from an 18-fold faster decoding than the graph to graph state of the art, while achieving a similar performance. We then successfully optimize the generation of molecules toward high docking scores, enabling a 10-fold enrichment of high-scoring compounds found with a fixed computational cost.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it