Protein sequence optimization on AAV
0.727Mean Fitness (Top 100 Sequences)SILO
Evaluation results
| Method | Links | ||
|---|---|---|---|
| SILOnumber of sequences=top 100, runs=52026.05 | 0.727 | — | |
| ProSperonumber of sequences=top 100, runs=52026.05 | 0.679 | — | |
| GFN-AL-δCSnumber of sequences=top 100, runs=52026.05 | 0.648 | — | |
| AdaLeadnumber of sequences=top 100, runs=52026.05 | 0.644 | — | |
| PEXnumber of sequences=top 100, runs=52026.05 | 0.62 | — | |
| BOnumber of sequences=top 100, runs=52026.05 | 0.618 | — | |
| LatProtRLnumber of sequences=top 100, runs=52026.05 | 0.563 | — | |
| MLDEnumber of sequences=top 100, runs=52026.05 | 0.555 | — | |
| CMA-ESnumber of sequences=top 100, runs=52026.05 | 0 | — | |
| DynaPPOnumber of sequences=top 100, runs=52026.05 | 0 | — | |
| CbASnumber of sequences=top 100, runs=52026.05 | 0 | — | |
| GFN-ALnumber of sequences=top 100, runs=52026.05 | 0 | — | |
| AdaLeadActive learning rounds=10, Independent runs=5, Top sequences=1002026.05 | — | 0.661 | |
| GFN-AL-δCSActive learning rounds=10, Independent runs=5, Top sequences=1002026.05 | — | 0.638 | |
| MLDEActive learning rounds=10, Independent runs=5, Top sequences=1002026.05 | — | 0.533 | |
| PEXActive learning rounds=10, Independent runs=5, Top sequences=1002026.05 | — | 0.603 | |
| ProSperoActive learning rounds=10, Independent runs=5, Top sequences=1002026.05 | — | 0.632 | |
| SILOActive learning rounds=10, Independent runs=5, Top sequences=1002026.05 | — | 0.727 |