Titulo Estágio
LEADD - deep LEarning Applied to Drug Discovery
Áreas de especialidade
Sistemas Inteligentes
Engenharia de Software
Local do Estágio
Laboratório de Computação Adaptativa (LARN)
Enquadramento
The traditional drug discovery process may take up to 15 years from conceptualization to market with a cost that can reach one thousand million dollars, without any warranties that the identified compounds will reach the market. The first three stages, namely target identification, lead discovery, and lead optimization, may take 4 to 7 years alone. This is mainly a data-driven process that starts with all human proteins that can be used as putative targets, the millions of lead compounds that need to be evaluated and, for the final candidates, a massive number of structural variants to be tested.
LEADD proposes the use of state-of-the-art Deep Learning methods to tackle the challenges identified on each of the initial stages of the drug discovery pipeline. Deep networks were proven to be more effective than shallow architectures to face complex problems like speech or image recognition. In addition, deep architectures are able to amplify key discriminative aspects from the input data while suppressing irrelevant information, thus attaining improved accuracy.
In this context, it is important to construct a database with relevant information regarding the Drug Discovery pipeline, in order to enable its analysis and the development of predictive models.
Objetivo
The main objective of this proposal is to develop a predictive model for improved implementation of the Drug Discovery pipeline.
The main goal is to develop a model able to apply Deep Learning Techniques to Drug Discovery:
(i) Construct the data set for the predictive model;
(ii) Perform Data Pre-Processing, Normalization and Scaling;
(iii) Select appropriate ML algorithms for building the Predictive Model;
(iv) Perform Sampling and Model Evaluation;
(v) Validate the overall Model with real data.
Plano de Trabalhos - Semestre 1
•Overview Drug Discovery, including target identification, lead discovery, and lead optimization;
•Overview of Deep Learning techniques, namely Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs) and Convolutional Neural Networks (CNN);
•Propose initial predictive model
•Prepare the intermediate report.
Plano de Trabalhos - Semestre 2
•Select, and preprocess a collection of large datasets for experiments;
•Study, and select, machine learning (ML) algorithms and feature selection (FS) algorithms for building the predictive model for detecting the occurrence of defects in metal components;
•Analyze experimental results: e.g., study parameter values; compare performance of the reduced datasets vs. previous results, etc.;
•Prepare a research paper;
•Prepare the final version of the thesis.
Condições
This work will be carried out in the Laboratory of Neural Networks (LARN) of CISUC, where there will be a regular supervision and feedback on the behalf of the supervisors.
Familiarity with machine learning and data mining algorithms and software tools are essential. Participating students will acquire valuable knowledge and experience with model building and data science by mining massive datasets, which skills are currently in high demand for various technology employers due to the relevance to various applications.
Observações
Scholarships will be available for students that show commitment to the work plan. Interested students are invited to contact the supervisors.
Logistics @Laboratory of Neural Networks (LARN)
DEI-FCTUC
Orientadores:
Joel P. Arrais (jpa@dei.uc.pt)
Bernardete Ribeiro (bribeiro@dei.uc.pt)
Orientador
Bernardete Ribeiro
bribeiro@dei.uc.pt 📩