Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2017-11-21T17:02:34Z No. of bitstreams: 1 Alan_Diego_dos_Santos_dis.pdf: 1881856 bytes, checksum: cf0113b0b67e0771e4b2920440d41e2b (MD5) / Rejected by Caroline Xavier (caroline.xavier@pucrs.br), reason: Devolvido devido ? falta da folha de rosto (p?gina com as principais informa??es) no arquivo PDF, passando direto da capa para a ficha catalogr?fica. on 2017-11-29T19:03:08Z (GMT) / Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2017-11-30T15:50:58Z No. of bitstreams: 1 Alan_Diego_dos_Santos_dis.pdf: 1884320 bytes, checksum: 6e508a972289e66527fd4b76cbae3586 (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-12-04T16:14:52Z (GMT) No. of bitstreams: 1 Alan_Diego_dos_Santos_dis.pdf: 1884320 bytes, checksum: 6e508a972289e66527fd4b76cbae3586 (MD5) / Made available in DSpace on 2017-12-04T16:18:35Z (GMT). No. of bitstreams: 1 Alan_Diego_dos_Santos_dis.pdf: 1884320 bytes, checksum: 6e508a972289e66527fd4b76cbae3586 (MD5) Previous issue date: 2017-03-29 / Triagem virtual de bancos de dados de ligantes ? amplamente utilizada nos est?gios iniciais do processo de descoberta de f?rmacos. Abordagens computacionais ?docam? uma pequena mol?cula dentro do s?tio ativo de um estrutura biol?gica alvo e avaliam a afinidade das intera??es entre a mol?cula e a estrutura. Todavia, os custos envolvidos ao aplicar algoritmos de docagem molecular em grandes bancos de ligantes s?o proibitivos, dado a quantidade de recursos computacionais necess?rios para essa execu??o. Nesse contexto, estrat?gias de aprendizagem de m?quina podem ser aplicadas para ranquear ligantes baseadas na afinidade com determinada estrutura biol?gica e, dessa forma, reduzir o n?mero de compostos qu?micos a serem testados. Nesse trabalho, propomos um modelo para ranquear ligantes baseados na arquitetura de redes neurais siamesas. Esse modelo calcula a compatibilidade entre receptor e ligante usando grades de propriedades bioqu?micas. N?s tamb?m mostramos que esse modelo pode aprender a identificar intera??es moleculares importantes entre ligante e receptor. A compatibilidade ? calculada baseada em rela??o ? conforma??o do ligante, independente de sua posi??o e orienta??o em rela??o ao receptor. O modelo proposto foi treinado usando ligantes ativos previamente conhecidos e mol?culas chamarizes (decoys) em um modelo de receptor totalmente flex?vel (Fully Flexible Receptor - FFR) do complexo InhA-NADH da Mycobacterium tuberculosis, encontrando ?timos resultados. / 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.