Predicting Drug Target Interaction Using Deep Belief Network

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With the advancement in AI field, machine learning methods are being used to train the classifier for separating intractable drug-target pair as it is difficult to classify dockable and non-dockable ligands due to non-linear nature of big-biological data. As deep learning has been shown to produce state-of-the-art results on various tasks, we propose a new approach to predict the interaction between drug and targets efficiently. The DBN is used to extract the high level features from 2D chemical substructure represented in fingerprint format. DBN is trained in a greedy layer-wise unsupervised fashion and the result from this pre-training phase is used to initialize the parameters prior to BP used for fine tuning. Similarly, logistic regression layer is staked as output layer. Then it is fine-tuned using BP of error derivative to build classification model that directly predict whether a drug interacts with a target of interest or not. In addition to this we too propose an approach to reduce the time complexity of training the learning method with the use of GPU which is highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart.

Education

PREDICTING DRUG TARGET
INTERATION USING ADAPTIVE
BELIEF NETWORK
Rashim Dhaubanjar (BCT/070/129)
Rupika Bista (BCT/070/131)
Shiva Gautam (BCT/070/192)
Sunil Bist (BCT/070/192)

INTRODUCTION
• Drug development - an expensive and time-consuming process with
extremely low success rate
• A core problem in pharmacology is the determination of interactions
between drug compounds and target proteins in order to understand
and study their effects.
• In this project we generalize tge applicability of the method so called
PDTI for drug compounds for which no interactions are known.

PROBLEM STATEMENT
• Very low known drug-target interactions
• No model with maximum accuracy in drug-target interaction with
lowest side-effect provision
• Time complexity

OBJECTIVES
• Build PDTI using Adaptive Deep Belief Network framework
• Apply GPU for the parallel implementation of different phases of
Deep Belief Network
• Evaluate the model by applying cross-validation technique with
standard dataset

Deep Belief Networks (DBN)
• Stack of several RBMs together.

Restricted Boltzmann Machines
activation f((weight w * input x) + bias b ) = output a

DBNs can be trained by a greedy layer-wise
approach:
• Train first layer using your data without the labels (unsupervised).
• Freeze the first layer parameters and start training the second layer
using the output of the first layer as the unsupervised input to the
second layer.
• Use the outputs of the final layer as inputs to a supervised
layer/model and train the last supervised layer(s) (leave early
weights frozen).
• Unfreeze all weights and fine tune the full network by training with a
supervised approach, given the pre-processed weight settings.

Feed-forward nets
Information flow is unidirectional
Data is presented to Input layer
Passed on to Hidden Layer
Passed on to Output layer
Information is distributed
Information processing is parallel
Internal representation (interpretation) of data

Backpropagation
• The Backpropagation algorithm is a sensible approach for dividing the
contribution of each weight.

GPU Implementation
• Training a DBN is a computationally expensive task that involves
training several RBMs and may require a considerable amount of time

GPU: GeForce 840M
Cores / Texture
CUDA: 5.0
CUDA cores: 384

Linear Vs Matrix(GPU)
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Iteration 1 Iteration 5 Iteration 10
Chart Title
Linear Matrix(GPU)

Linear Vs Matrix(GPU)
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Iteration 1 Iteration 5 Iteration 10
Linear Matrix(GPU)

LANGUAGES USED
• Java – Implementation of Algorithms
• JCuda – Programming for GPU
• VBA – Integration with Excel

Work Complete
• completed the process of algorithm development followed by testing
and debugging of the developed algorithm and also implemented in
code.

Work Remaining
• develop a proper GUI where input will be the name of drug and the
output will be interaction with protein (1 if yes and 0 if no).

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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency. Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor. His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects. What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results. Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment. Date: May 29, 2024 Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR ------------------------------------------------------------------------------- Find out more about ISO training and certification services Training: ISO/IEC 27001 Information Security Management System - EN | PECB ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB General Data Protection Regulation (GDPR) - Training Courses - EN | PECB Webinars: https://pecb.com/webinars Article: https://pecb.com/article ------------------------------------------------------------------------------- For more information about PECB: Website: https://pecb.com/ LinkedIn: https://www.linkedin.com/company/pecb/ Facebook: https://www.facebook.com/PECBInternational/ Slideshare: http://www.slideshare.net/PECBCERTIFICATION

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1. PREDICTING DRUG TARGET INTERATION USING ADAPTIVE BELIEF NETWORK Rashim Dhaubanjar (BCT/070/129) Rupika Bista (BCT/070/131) Shiva Gautam (BCT/070/192) Sunil Bist (BCT/070/192)

2. INTRODUCTION • Drug development - an expensive and time-consuming process with extremely low success rate • A core problem in pharmacology is the determination of interactions between drug compounds and target proteins in order to understand and study their effects. • In this project we generalize tge applicability of the method so called PDTI for drug compounds for which no interactions are known.

3. PROBLEM STATEMENT • Very low known drug-target interactions • No model with maximum accuracy in drug-target interaction with lowest side-effect provision • Time complexity

4. OBJECTIVES • Build PDTI using Adaptive Deep Belief Network framework • Apply GPU for the parallel implementation of different phases of Deep Belief Network • Evaluate the model by applying cross-validation technique with standard dataset

5. Deep Belief Networks (DBN) • Stack of several RBMs together.

6. Restricted Boltzmann Machines activation f((weight w * input x) + bias b ) = output a

7. Reconstructions

8. DBNs can be trained by a greedy layer-wise approach: • Train first layer using your data without the labels (unsupervised). • Freeze the first layer parameters and start training the second layer using the output of the first layer as the unsupervised input to the second layer. • Use the outputs of the final layer as inputs to a supervised layer/model and train the last supervised layer(s) (leave early weights frozen). • Unfreeze all weights and fine tune the full network by training with a supervised approach, given the pre-processed weight settings.

9. Unsupervised Pretraining

10. Feed-forward nets Information flow is unidirectional Data is presented to Input layer Passed on to Hidden Layer Passed on to Output layer Information is distributed Information processing is parallel Internal representation (interpretation) of data

11. Backpropagation • The Backpropagation algorithm is a sensible approach for dividing the contribution of each weight.

12. GPU Implementation • Training a DBN is a computationally expensive task that involves training several RBMs and may require a considerable amount of time

13. Solution? • GPU Parallel implementation

14. GPU: GeForce 840M Cores / Texture CUDA: 5.0 CUDA cores: 384

15. Linear Vs Matrix(GPU) 0 100 200 300 400 500 600 700 800 900 1000 Iteration 1 Iteration 5 Iteration 10 Chart Title Linear Matrix(GPU)

16. Linear Vs Matrix(GPU) 0 100 200 300 400 500 600 700 800 900 1000 Iteration 1 Iteration 5 Iteration 10 Linear Matrix(GPU)

17. TASK DIVISION

18. LANGUAGES USED • Java – Implementation of Algorithms • JCuda – Programming for GPU • VBA – Integration with Excel

19. LIMITATIONS • Protein sequence feature domain profile not used in training purpose • Only drug compound 2D substructure finger print used as pretraining dataset.

20. Work Complete • completed the process of algorithm development followed by testing and debugging of the developed algorithm and also implemented in code.

21. Work Remaining • develop a proper GUI where input will be the name of drug and the output will be interaction with protein (1 if yes and 0 if no).

22. THANK YOU

Editor's Notes

At node 1 of the hidden layer, x is multiplied by a weight and added to a so-called bias. The result of those two operations is fed into an activation function, which produces the node’s output, or the strength of the signal passing through it, given input x.
But in this introduction to restricted Boltzmann machines, we’ll focus on how they learn to reconstruct data by themselves in an unsupervised fashion (unsupervised means without ground-truth labels in a test set), making several forward and backward passes between the visible layer and hidden layer no. 1 without involving a deeper network. In the reconstruction phase, the activations of hidden layer no. 1 become the input in a backward pass. They are multiplied by the same weights, one per internode edge, just as x was weight-adjusted on the forward pass. The sum of those products is added to a visible-layer bias at each visible node, and the output of those operations is a reconstruction; i.e. an approximation of the original input. This can be represented by the following diagram:
Pre-training a DNN. The first row represents the first layer being trained to reproduce X, the second row is where the first layer’s weights are fixed and the second layer is trained to reproduce the output of the first layer, and so on. Finally we make use of the supervised data to learn all the weights after they have been initialised to the weights learnt in the previous step
The ideas of the algorithm is: Computes the error term for the output units using the observed error. 2. From output layer, repeat propagating the error term back to the previous layer and updating the weights between the two layers until the earliest hidden layer is reached.

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