A Pharmacophore-Based Method for Rapid and Accurate Virtual Screening of Antibody Libraries against Antigens
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Antibody-based biotherapeutics make up an important class of biopharmaceuticals. However, their discovery requires resource- and time-consuming laboratory processes. To ameliorate this situation, several computational methods were used to predict the structures of antibody:antigen complexes (Ab:Ag) and identify potential binders, in-silico. However, there is still a general lack of rapid virtual screening methods capable of screening large antibody libraries against a given antigen or group of antigens. In this work, we explore the application of a successful small-molecule drug discovery strategy and adapt pharmacophore-based virtual screening to the world of antibody discovery. Using a nonredundant data set of 874 Ab:Ag complexes, we have developed an automated method to create pharmacophores from the antibody complementarity determining regions. Our method is 98.6% (862 out of 874) successful at reproducing the ground truth, i.e., it can recapitulate the parental antibody:antigen complexes. In a benchmarking comparison with cognate docking, using 33 Ab:Ag complexes of therapeutic interest, the pharmacophore method was not only much faster than cognate docking but also recovered all the native interfacial contacts. In addition, it can also find additional putative antibody binders to a given antigen within clusters of Ab:Ag complexes with similar interfacial structures. Our method has significant implications toward accelerating biotherapeutic drug discovery as well as drug repurposing research. This method was implemented in MOE 2024 and is available to the scientific community.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".