Customizable Generation of Synthetically Accessible, Local Chemical Subspaces
Bibliographic record
Abstract
Screening large libraries of chemicals has been an efficient strategy to discover bioactive compounds; however a portion of the potential for success is limited to the available libraries. Synergizing combinatorial and computational chemistries has emerged as a time-efficient strategy to explore the chemical space more widely. Ideally, streamlining the evaluation process for larger, feasible chemical libraries would become commonplace. Thus, combinatorial tools and, for example, docking methods would be integrated to identify novel bioactive entities. The idea is simple in nature, but much more complex in practice; combinatorial chemistry is more than the coupling of chemicals into products: synthetic feasibility includes chemoselectivity, stereoselectivity, protecting group chemistry, and chemical availability which must all be considered for combinatorial library design. In addition, intuitive interfaces and simple user manipulation is key for optimal use of such tools by organic chemists-crucial for the integration of such software in medicinal chemistry laboratories. We present herein Finders and React2D-integrated into the Virtual Chemist platform, a modular software suite. This approach enhances virtual combinatorial chemistry by identifying available chemicals compatible with a user-defined chemical transformation and by carrying out the reaction leading to libraries of realistic, synthetically accessible chemicals-all with a completely automated, black-box, and efficient design. We demonstrate its utility by generating ∼40 million synthetically accessible, stereochemically accurate compounds from a single library of 100 000 purchasable molecules and 56 well-characterized chemical reactions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".