Publications
Talk · IEEE International Conference on Application of Information and Communication Technologies · October 7, 2020
Using Support Vector Regression in multi-target prediction of drug toxicity
Fatima Аdilova, Alisher Ikramov
Abstract
We consider the task of drug activity prediction, specifically we predict the toxicity of fullerene-based nanoparticles in interaction with 1117 proteins. We use a multi-target Support Vector Regression model with a greedy feature selection technique to achieve RMSE of 362.9 on a test set. We also demonstrate the impact of hyperparameter tuning on model performance.
Computational Drug Discovery MethodsMachine Learning in BioinformaticsMachine Learning in Materials Sciencedrug toxicity predictionsupport vector regressionmulti-target prediction
More by Fatima Adilova
- Graph-based density-variation outlier removal for chronic disease prediction: A leakage-free benchmark and component-wise ablationResults in Engineering · 2026
- Monotonic System Framework for Globally Optimal Structuring of Complex Data2026
- Boosting LLM Adaptation: Superior Stability and Convergence via OLoRA+ Across Diverse NLP BenchmarksInternational Siberian workshops and tutorials on electron devices and materials · 2026
- Генерации графов заданной структуры: от глубоких нейронных сетей к квантовым моделям (на примере создания новых лекарств)Problems of Computational and Applied Mathematics · 2026