Ranking ligands in structure-based virtual screening using siamese neural networks

• Ano de defesa: 2017
• Autor(a) principal: Santos, Alan Diego dos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul; Faculdade de Informática; Brasil; PUCRS; Programa de Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Triagem Virtual; Redes Neurais Siameses; Funções de Escore; Docagem Molecular; Virtual Screening; Siamese Neural Network; Scoring Function; Molecular Docking; CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/7763

Structure-based virtual screening (SBVS) on compounds databases has been widely applied in early stage of the drug discovery on drug target with known 3D structure. In SBVS, computational approaches usually ’dock’ small molecules into binding site of drug target and ’score’ their binding affinity. However, the costs involved in applying docking algorithms into huge compounds databases are prohibitive, due to the computational resources required by this operation. In this context,different types of machine learning strategies can be applied to rank ligands, based on binding affinity,and to reduce the number of compounds to be tested. In this work, we propose a deep learning energy-based model using siamese neural networks to rank ligands. This model takes as inputs grids of biochemical properties of ligands and receptors and calculates their compatibility. We show that the model can learn to identify important biochemical interactions between ligands and receptors. Besides, we demonstrate that the compatibility score is computed based only on conformation of small molecule, independent of its position and orientation in relation to the receptor. The proposed model was trained using known ligands and decoys in a Fully Flexible Receptor model of InhA-NADH complex (PDB ID: 1ENY), having achieved outstanding results.