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BioChemAIgent: An AI-driven Protein Modeling and Docking Framework for Structure-Based Drug Discovery

Yousefi, B.; Laubach, N. C.; Heins, S.; Testa, L.; Gersting, S. W.; Bonn, S.

2025-12-19 bioinformatics
10.64898/2025.12.17.694892 bioRxiv
Show abstract

Recent advances in AI have substantially accelerated in silico drug discovery, yet most computational approaches remain task-specific and require expert knowledge. We present BioChemAIgent, an agentic framework that orchestrates state-of-the-art AI models and established computational chemistry tools to support end-to-end small-molecule analysis, protein modeling, molecular docking, and interaction analysis through a unified interface. BioChemAIgent emphasizes transparent reasoning, reproducible workflows, and community-oriented extensibility for structural biology and drug discovery applications.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 0.2%
22.4%
Similar papers in this journal
2
Bioinformatics
◐ 1204 papers in training set
Top 1%
18.9%
Similar papers in this journal
3
Journal of Cheminformatics
○ 29 papers in training set
Top 0.1%
12.2%
Similar papers in this journal
50% of probability mass above
4
Bioinformatics Advances
◐ 203 papers in training set
Top 0.4%
8.1%
Similar papers in this journal
5
Nature Communications
○ 5641 papers in training set
Top 30%
4.4%
Similar papers in this journal
6
Briefings in Bioinformatics
◐ 354 papers in training set
Top 2%
4.1%
7
Journal of Molecular Biology
○ 232 papers in training set
Top 1%
2.7%
8
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.4%
9
Nature Methods
○ 385 papers in training set
Top 4%
2.2%
10
Protein Science
○ 246 papers in training set
Top 2%
1.9%
11
Scientific Reports
○ 3612 papers in training set
Top 52%
1.7%
12
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.5%
13
Patterns
○ 78 papers in training set
Top 2%
1.4%
14
Nucleic Acids Research
◐ 1281 papers in training set
Top 10%
1.4%
15
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.0%
16
mAbs
○ 32 papers in training set
Top 0.5%
0.6%
17
iScience
○ 1154 papers in training set
Top 39%
0.6%
18
Nature Protocols
○ 33 papers in training set
Top 0.6%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.