Overview / Usage

An Overview

DrugForge is an advanced computational platform that helps speed up and optimize early-stage research into drug discovery through advanced AI and ML models. Traditional drug discovery is a resource-demanding, time-consuming, and expensive process, getting in excess of 10 years and billions of dollars to bring a single product to market. Moreover, high failure rates of drug candidates during later stages due to poor pharmacokinetic or toxicological profiles have complicated matters further.

DrugForge makes an attempt to solve the aforementioned problems by providing AI-driven predictive models interfaced via high-speed computing frameworks like Intel OneAPI and Sklearnex; facilitating analysis of large datasets and thus providing opportunity for assessing critical safety end-points early in the drug development pipeline.

Problems that Are Being Cured

1. Inefficiency into the e-Drug Discovery Process:

• For drug development up to 12 years or more, it is costly and time-consuming.

• Most of the failures in the later stages of drug development are due to the undetected problems of solubility, toxicity, and pharmacokinetics.

1. Resource Constraints:

Access to high-performance computational tools is restricted to smaller research groups and less than prestigious institutions. Long computational time spent on analyzing large chemical libraries, using traditional methods.

1. Accuracy and Precision in Predictions:

Correct prediction of the molecular property which includes solubility, blood-brain barrier permeability (BBBP), toxicity, half life, and interaction between drug and enzyme.

1. Limited Scalability:

-Most of the existing computation approaches are unable to deal with big data sets so as to open larger prospects for exploring chemical spaces.

Sample usage of DrugForge includes but is not limited to:

1)Pharmaceutical Companies:

-to set up a preliminary screening of drug candidates for pharmacokinetic and toxicological properties;

-avoid wasting resources on compounds that are likely to fail in the later stages of drug development;

2)Academic Research:

-Give cost effective and scalable computational tools to small research teams;

-Diverse chemical spaces for researchers; couriering multi-target-drug interaction experiments.

3)Biotech Startups:

-Accelerate timelines of drug discovery with cost-efficient resource above high-throughput screening.

-Drive innovative drug re-purposing efforts.

4)Evergreen Global Health Initiatives:

-Quickly screening candidates for therapeutic interventions in new infectious diseases or even rare diseases.

-Democratizing advanced AI tools for contributing towards global drug discovery efforts.

Methodology / Approach

The creation of the DrugForge tool follows an integrated approach of cheminformatics, advanced machine learning (ML) techniques, and high-performance computing (HPC) technologies. These methods are judiciously selected against existing inefficiencies such as high costs, long timelines, and resource constraints in traditional drug discovery processes.

1. Data Gathering and Preprocessing

Approach:

Data Sources: Molecular data will be derived from publicly available databases such as ChEMBL, PubChem, and DrugBank. These provide structural formats such as SMILES strings and PDB files.

Preprocessing:

Removes duplicate, incomplete, and erroneous structures to ensure a standard of quality in the data.

Uses RDKit: Will be used to calculate various molecular descriptors; such as molecular weight, hydrophobicity (LogP), topological polar surface area (TPSA), hydrogen bond donors (HBD), and hydrogen acceptors (HBA); as input feature for ML models.

2.Predictive Models:

Approach:

Feature Engineering: Molecular descriptors are carefully selected based on their relevance to specific pharmacokinetic and toxicological properties.

Machine Learning Techniques:

-Random Forests (RF): used for classification tasks such as solubility determination or BBB permeability.

-Support Vector Machines (SVM): usually applied for complex regression tasks; they predict half-life or CYP3A4 inhibition.

-Gradient Boosting Algorithms (e.g., XGBoost): have been optimized for both classification and regression to achieve high accuracy and eliminate false positives/negatives.

Hyperparameter Tuning:

-Grid search and cross-validation techniques optimize model performance (precision, recall, specificity, accuracy).

Task Specific Models:

Predicting Solubility.

Blood Brain Barrier Permeability (BBBP).

Toxicity Prediction.

Half Life Estimating.

CYP3A4 Inhibition Prediction.

** 3. High-performance Computing Integrations Approach:**

In the future, it will probably become Intel OneAPI-accelerating workflows through parallel processes and optimizing the hardware.

-Scikit-learn Extensions (Sklearnex):

Optimizing machine learning algorithm executions for the Intel architecture.

Improving memory management capabilities to handle big data.

-Performance Increases:

from 78.94 seconds to 44.76 seconds for solubility prediction runtime, which is worth a 43.3% improvement. BBB permeability model runtime reduced by up to 92.6%.

Technologies Used

Libraries and Frameworks

1)Cheminformatics Tools:

- **RDKit**: Extracts molecular descriptors and chemical properties.
- **Open Babel**: Converts molecular file formats and supports chemical data operations.

2)Machine Learning Frameworks:

- **Scikit-learn**: For implementing machine learning models.
- **XGBoost**: High-performance gradient boosting for classification and regression.
- **TensorFlow/PyTorch** (if applicable): For any deep learning tasks or extensions.

3)Optimization Libraries:

- **Intel Sklearnex**: Optimized extensions for Scikit-learn to enhance performance on Intel hardware.

4)Data Preprocessing and Handling:

- **Pandas**: For structured data manipulation.
- **NumPy**: For numerical computations.
- **Matplotlib** & **Seaborn**: For visualizing predictive results.

5)Molecular Visualization:

- **PyMOL**: For molecular docking and interaction visualizations.
- **Chimera**: For detailed 3D structural visualizations (if applicable).

Intel Technologies

1)Intel OneAPI:
Provides optimized parallel computing capabilities, enabling efficient execution of resource-intensive tasks.

2)Intel Sklearnex:
Accelerates Scikit-learn workflows using Intel hardware-specific optimizations.

3)Intel Processors:
Development and testing on systems powered by Intel Core i7/i9 processors, offering multi-threading and parallel processing capabilities.

4)Intel Math Kernel Library (MKL):
Enhances numerical computing performance.

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Repository

https://github.com/Aashik1701/Drug_Discovery_with_Intel_AI