Title

DEEP LEARNING FOR PREDICTION OF DRUG-TARGET INTERACTION SPACE AND PROTEIN FUNCTIONS

Abstract

Abstract

With the advancement of sequencing and high-throughput screening
technologies, large amount of sequence and compound data have been
accumulated in biological and chemical databases.However, only small
number of proteins and compounds have been annotated by wet-lab
experiments due to the huge compound and chemical space.In this thesis,
we describe the design and implementation of several methods for
drug-target interaction prediction and functional annotations of
proteins.The first method, DEEPred is a sequence based automated protein
function prediction method that employs a stacked multi-task deep neural
networks based on Gene Ontology directed acyclic graph
hierarchy.DEEPScreen is the second method and it is a drug-target
interaction (binary) prediction method.In DEEPScreen, the idea is to
learn compound features automatically using compound images via
convolutional neural networks.The third method is called MDeePred which
is a binding affinity prediction method.MDeePred is a chemogenomic
method where both protein and compound features were fed to a hybrid
pairwise deep neural network structure.The main difference between
MDeePred and DEEPScreen in terms of features is that MDeePred employs
compound-target feature pairs whereas in DEEPScreen only compound
features were used.The main novelty of MDeePred is the proposed
multi-channel featurization approach for protein sequences where each
channel represents a different property of input protein sequences.The
fourth method is called iBioProVis which is an online interactive
visualization tool for chemical space.The main purpose of iBioProVis is
to embed and visualize compound features on 2-D space.The inputs for
iBioProVis are target protein identifiers and optionally, SMILES strings
of user-input compounds.The tool then generates circular fingerprints
for active compounds of targets and user-input compounds and then,
t-Stochastic Neighbor Embedding method is used to embed compounds on 2-D
space.

Biography:

Supervisor(s)

Supervisor(s)

AHMET SUREYYA RIFAIOGLU

Date and Location

Date and Location

2020-06-30;16:00:00-Webinar

Category

Category

PhD_Thesis