DEEP LEARNING-ASSISTED DRUG DISCOVERY PLATFORM

Abstract

The present invention introduces a Deep Learning-Assisted Drug Discovery Platform that leverages advanced neural network architectures to accelerate and enhance the identification of potential drug candidates. The platform integrates modules for data preprocessing, feature extraction, predictive modeling, and generative modeling to analyze multi-omics datasets and predict drug-target interactions, efficacy, and toxicity. It employs deep learning models to automatically extract relevant features from complex biological data and utilizes generative neural networks for virtual screening and lead compound generation. The system includes a real-time feedback loop for model refinement and a user-friendly interface for visualization, significantly reducing the time and cost of traditional drug discovery processes.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The present invention is a Deep Learning-Assisted Drug Discovery Platform designed to enhance and streamline the drug discovery process by leveraging advanced deep learning algorithms. The platform integrates multiple components, including data preprocessing, feature extraction, predictive modeling, and generative modeling, to analyze complex biological datasets and provide actionable insights for drug discovery.

The Data Preprocessing Module is responsible for ingesting a variety of datasets, such as genomic sequences, proteomic profiles, and molecular descriptors. The module employs techniques such as normalization, noise reduction, and feature scaling to ensure the input data is clean and consistent. This preprocessing step is crucial for improving the quality of the data and enhancing the performance of subsequent deep learning models.

The Feature Extraction Module utilizes deep learning architectures like Convolutional Neural Networks (CNNs) and Transformer models to automatically identify and learn relevant features from the preprocessed data. Unlike traditional machine learning approaches that require manual feature engineering, this module can extract high-level features directly from raw data, capturing complex patterns and interactions between molecules and biological targets.
The Predictive Modeling Module is a core component of the platform that employs deep learning algorithms to predict key drug properties, such as binding affinity, efficacy, toxicity, and potential side effects. By using neural networks with multiple layers, the module is capable of handling high-dimensional data and making accurate predictions even in the presence of complex biological interactions. It can also integrate various data sources to enhance prediction accuracy, making it a versatile tool for drug discovery.

The Generative Modeling Module utilizes Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to generate novel molecular structures. This module facilitates virtual screening by creating new chemical compounds that are likely to exhibit desired pharmacological properties. The generative models can optimize these structures based on predefined criteria, such as minimizing toxicity or maximizing binding affinity, thus speeding up the lead optimization process.

The User Interface Module provides a user-friendly platform for researchers to interact with the system. It includes visualization tools that allow users to explore the predicted molecular structures, binding affinities, and other drug properties. The interface also offers an interactive dashboard for managing datasets, viewing model predictions, and analyzing the results. This helps researchers make informed decisions and refine their drug discovery strategies based on the insights provided by the platform.

The platform also features a Feedback Loop mechanism that continuously updates the deep learning models with new experimental data. This iterative process improves the accuracy and robustness of the models over time, making them more effective at predicting drug candidates in diverse biological contexts. By integrating this adaptive feedback, the platform remains up-to-date with the latest research findings and experimental outcomes, ensuring that its predictions are reliable and actionable.

In the first embodiment, the invention is applied to the identification of new inhibitors for a specific protein target involved in a disease pathway. The platform begins by collecting datasets from public biological databases, including protein sequences, 3D molecular structures, and known small-molecule inhibitors. The Data Preprocessing Module standardizes these datasets, removing noise and scaling the features.

The processed data is then fed into the Feature Extraction Module, where a Transformer-based neural network architecture learns relevant features, such as binding site characteristics and molecular fingerprints. The extracted features are passed to the Predictive Modeling Module, which uses a deep learning model to predict the binding affinity of various small molecules to the target protein. The top predicted candidates are further optimized in the Generative Modeling Module, where a Variational Autoencoder suggests modifications to enhance the binding affinity and reduce toxicity.

The results are presented to the researcher through the User Interface Module, displaying visualizations of the top candidate molecules and their predicted binding affinities. The researcher can select promising compounds for further experimental validation. This embodiment demonstrates the platform's capability to efficiently identify and optimize potential drug candidates for a specific protein target, significantly reducing the time and cost of traditional screening methods.

In the second embodiment, the invention is used for multi-target drug discovery, focusing on the development of compounds that can simultaneously modulate multiple biological targets implicated in complex diseases like cancer or Alzheimer's. The platform integrates datasets comprising genomic profiles, proteomic interactions, and existing multi-target drugs.

The Data Preprocessing Module normalizes and merges these heterogeneous datasets, creating a comprehensive input for the deep learning models. The Feature Extraction Module employs Convolutional Neural Networks to learn complex patterns in the multi-target interaction data, identifying key features associated with effective drug candidates.

The Predictive Modeling Module then evaluates potential compounds for their ability to bind effectively to multiple targets simultaneously, predicting efficacy and potential off-target effects. The Generative Modeling Module uses a Generative Adversarial Network to propose new molecular structures tailored to engage multiple targets, optimizing for properties such as solubility and minimal side effects.

Finally, the User Interface Module provides a detailed visualization of the predicted multi-target interactions, highlighting potential lead compounds and their predicted efficacy across different targets. Researchers can interact with the visualizations, adjust parameters, and select compounds for experimental testing. This embodiment illustrates the platform's ability to address complex drug discovery challenges by targeting multiple biological pathways simultaneously, offering a powerful tool for developing treatments for multifactorial diseases.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A method for drug discovery using a deep learning-assisted platform, the method comprising:
Collecting and preprocessing multi-omics datasets related to drug discovery;
Employing deep learning models to extract features from the processed data;
Predicting potential drug-target interactions and efficacy using the extracted features;
Generating novel molecular structures using generative neural network models;
Evaluating and optimizing the generated structures to identify potential drug candidates.

2.The method of claim 1, wherein the data preprocessing module includes normalization techniques such as Min-Max scaling and standardization to handle diverse datasets.

3.The method of claim 1, wherein the generative modeling module employs a Variational Autoencoder (VAE) to generate potential drug candidates with desired properties.

4.The method of claim 1, further comprising a step of integrating external biological databases for enhanced prediction accuracy and model validation.

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