인공지능 기반 단백질-리간드 상호작용 예측을 위한 신약 후보 물질 발굴 프레임 워크
AI-Driven Prediction of Protein–Ligand Interactions for Accelerated Drug Discover
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초록
본연구는단백질–리간드상호작용예측에서구조기반접근의한계를극복하고자,단백질서열기반의입력만으로도예측이가능한경량인공지능기반결합친화도예측프레임워크를제안한다.리간드는BindingDB에서수집한12,309개의 SMILES 데이터를 2048차원의 Morgan Fingerprint로 인코딩하고, 단백질은 AAC(20차원)으로 표현하였다. 입력값으로XGBoost 회귀모델을구성하고, 예측값과실제값의차이를보정하는2단계회귀모델을구현하였다. 최종예측값은RMSE0.90, 결정계수(R²) 0.74, 정확도 88%, AUC 0.948의 성능을 보였으며, SHAP 해석을통해 주요아미노산조성과서브스트럭처가예측에영향을미침을확인했다.본프레임워크는약물재창출및가상스크리닝에서 구조기반모델을보완하는도구로활용할수있다.
Abstract
This study proposes a lightweight artificial intelligence-based prediction framework to address the limitations of structure-based methods for predicting protein–ligand interactions. Unlike conventional approaches, the model relies exclusively on sequence-based inputs. A total of 12,309 ligand molecules were retrieved from BindingDB and encoded as 2,048-dimensional Morgan fingerprints derived from SMILES notation. Protein sequences were represented as 20-dimensional amino acid composition (AAC) vectors. These embeddings were used as inputs to an XGBoost regression model to predict binding affinity (pKi), followed by a second-stage regression model that calibrated predictions based on residual errors. The final calibrated framework achieved strong predictive performance, with an RMSE of 0.90, R² of 0.74, a binary classification accuracy of 88%, and an AUC of 0.948. Furthermore, shapley additive explanations (SHAP) highlighted the contributions of specific amino acid features —such as tryptophan (AAC_W) and histidine (AAC_H)—as well as ligand substructures, to the predicted binding affinity. By enabling rapid and accurate predictions without requiring 3D protein structures, this framework provides a versatile complement to structure-based methods and offers practical utility for applications such as drug repurposing and virtual screening.