METHOD FOR THE DEVELOPMENT AND SCREENING OF SMALL MOLECULE LIBRARIES FOR DRUG DISCOVERY

Abstract

METHOD FOR THE DEVELOPMENT AND SCREENING OF SMALL MOLECULE LIBRARIES FOR DRUG DISCOVERY ABSTRACT The present invention provides a method for the development and screening of small molecule libraries for drug discovery, integrating computational techniques with traditional high-throughput screening. This method encompasses the construction of pharmacophore models, computational generation of diverse small molecule libraries, virtual screening against biological targets, synthesis of top candidates, and in vitro biological assays for activity validation. Machine learning algorithms are utilized to analyze results and iteratively refine the library based on feedback, enhancing the likelihood of identifying viable drug candidates. This approach significantly reduces the time and cost associated with drug discovery, making it particularly advantageous for complex diseases.

Specification

Description:METHOD FOR THE DEVELOPMENT AND SCREENING OF SMALL MOLECULE LIBRARIES FOR DRUG DISCOVERY

FIELD OF THE INVENTION

The present invention relates to the field of pharmaceutical sciences and drug discovery. More specifically, the invention pertains to methods for developing and screening libraries of small molecules that can be utilized to identify potential therapeutic agents for various diseases. The methods described herein enable the efficient and systematic identification of bioactive compounds with desirable pharmacological properties, accelerating the drug discovery process.
BACKGROUND OF THE INVENTION

The discovery of new drugs is a complex, time-consuming, and costly endeavor, often involving the screening of vast libraries of chemical compounds against specific biological targets. Traditionally, this process has relied on high-throughput screening (HTS) techniques, which, while effective, can lead to high rates of false positives and negatives due to limitations in assay design and compound solubility. Moreover, many existing methods do not adequately consider the molecular properties that contribute to bioavailability and selectivity, leading to an increased attrition rate in drug development.
The rise of combinatorial chemistry has provided a means to rapidly generate diverse libraries of small molecules; however, without robust screening methodologies, many of these compounds remain untested or mischaracterized. Furthermore, the integration of computational techniques and data analytics into drug discovery processes has emerged as a critical area of focus. Advanced algorithms and machine learning models can predict the activity of compounds based on their chemical structure, potentially streamlining the screening process and reducing the number of compounds that require physical testing.
Recent advancements in bioinformatics and cheminformatics offer tools to better understand the interaction of small molecules with biological targets, allowing for more informed decisions during the selection and optimization stages. However, there remains a need for a comprehensive method that combines these innovative approaches with traditional screening methods, enabling the rapid identification of promising drug candidates while minimizing time and resource expenditures.

SUMMARY OF THE INVENTION

The present invention provides a method for the development and screening of small molecule libraries for drug discovery that integrates advanced computational techniques with traditional high-throughput screening methods. This novel approach utilizes a combination of virtual screening, structure-activity relationship (SAR) analysis, and experimental validation to generate and evaluate a diverse library of small molecules efficiently.
In an embodiment of the invention, the method begins with the computational generation of small molecule libraries based on predefined pharmacophore models. These libraries are subjected to virtual screening against selected biological targets to predict binding affinities and selectivity. The top-ranking candidates are then synthesized and subjected to in vitro biological assays to confirm their activity.
The method further incorporates machine learning algorithms to analyze screening results and refine the library based on the feedback obtained. This iterative process not only enhances the efficiency of the drug discovery pipeline but also increases the likelihood of identifying compounds with favorable pharmacokinetic and pharmacodynamic properties.
The invention is particularly advantageous for the rapid identification of drug candidates for complex diseases, including cancer, neurodegenerative disorders, and infectious diseases, where traditional methods often fall short.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying figures where like reference numerals refer to identical or functionally similar elements throughout the separate views and which together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the invention.
The accompanying figure illustrates the workflow of the method for the development and screening of small molecule libraries. It depicts the sequential steps involved, including the computational generation of libraries, virtual screening processes, synthesis of top candidates, and subsequent biological assays. The figure also highlights the feedback loop from biological results to library refinement, illustrating the iterative nature of the method.
Skilled artisans will appreciate the elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed. It shall be understood that different aspects of the invention can be appreciated individually, collectively, or in combination with each other.
The method of the present invention begins with the identification of suitable biological targets for drug discovery, which may include proteins, enzymes, or other molecular entities implicated in disease processes. Once the target is selected, a pharmacophore model is constructed based on known ligand-target interactions, defining the key structural features necessary for binding.
With the pharmacophore model established, a diverse library of small molecules is computationally generated using combinatorial chemistry techniques. These libraries may consist of thousands to millions of unique compounds, enabling a broad range of structural diversity. The generated molecules are then screened using virtual screening methods, where their binding affinities are predicted through docking simulations and scoring functions.
After the virtual screening phase, the top candidates identified based on their predicted activities are synthesized in the laboratory. The synthesis process involves both automated and manual techniques, allowing for the efficient production of the compounds. Each synthesized compound is then subjected to a series of in vitro biological assays to assess its pharmacological activity, specificity, and potential toxicity.
In addition to traditional screening, the method employs advanced machine learning algorithms to analyze the results obtained from biological assays. By utilizing large datasets from previous screenings and biological interactions, these algorithms can identify patterns and optimize the selection process for future library generations.
Feedback from the biological results is crucial, as it informs adjustments to the pharmacophore model and library design, resulting in an iterative refinement of the small molecule libraries. This continuous cycle of generation, screening, and refinement significantly enhances the likelihood of success in identifying viable drug candidates.
Moreover, the integration of data analytics tools facilitates the organization and interpretation of screening results, enabling researchers to visualize structure-activity relationships and draw meaningful conclusions about compound efficacy and safety profiles.
The method can be applied to various therapeutic areas, including oncology, infectious diseases, and chronic conditions, and is adaptable to different biological targets. Its versatility allows for the prioritization of compounds with the highest potential for successful development into therapeutics.
Advantages of the Invention
The method for the development and screening of small molecule libraries for drug discovery offers several advantages over traditional approaches. Firstly, by combining computational and experimental techniques, the method significantly reduces the time and cost associated with drug discovery, enabling quicker identification of lead candidates.
Secondly, the use of virtual screening and machine learning enhances the accuracy of predictions regarding compound activity, thereby minimizing false positives and negatives. This leads to a more efficient selection process, focusing resources on the most promising candidates.
Additionally, the iterative refinement process ensures that libraries evolve based on experimental feedback, increasing the likelihood of discovering compounds with desirable pharmacological properties. This approach not only improves the success rate of drug candidates but also enables the exploration of novel chemical spaces, potentially leading to innovative therapeutic solutions.
Furthermore, the method is scalable, allowing for the handling of large libraries of compounds, which is essential in the modern drug discovery landscape where the need for diversity is paramount. By leveraging technological advancements, the invention aligns with contemporary trends in drug development, emphasizing efficiency and precision.
Embodiment 1: Targeted Drug Discovery for Cancer Therapy
In this embodiment, the method focuses on the discovery of small molecules targeting the epidermal growth factor receptor (EGFR) for cancer therapy. The pharmacophore model is constructed using known EGFR inhibitors, identifying critical structural features necessary for binding and activity. A diverse library of small molecules is generated, incorporating various chemical scaffolds predicted to interact with the EGFR. Virtual screening is performed using molecular docking simulations, identifying potential candidates with high predicted binding affinities. The top 50 compounds are synthesized and tested in vitro for anti-proliferative activity against EGFR-expressing cancer cell lines. Machine learning algorithms analyze the activity data to refine the pharmacophore model, enabling an iterative loop that enhances the selection of promising drug candidates.

Embodiment 2: Antiviral Drug Development
This embodiment applies the method to discover small molecules that inhibit viral replication in diseases such as influenza. The pharmacophore model is created based on known antiviral agents that target viral RNA polymerase. A combinatorial library of small molecules is generated, focusing on compounds that mimic the structure of the active site of the viral enzyme. After virtual screening and synthesis of the highest-ranking candidates, in vitro assays are performed to evaluate antiviral activity against influenza virus. The results are used to inform the refinement of the library, allowing the identification of novel compounds with enhanced efficacy.

Embodiment 3: Neurodegenerative Disease Modulators
In this embodiment, the method is adapted for discovering small molecules that modulate protein aggregation associated with neurodegenerative diseases like Alzheimer's. The pharmacophore model is developed based on known modulators of amyloid-beta aggregation. The library is generated using diverse chemical entities designed to penetrate the blood-brain barrier. Virtual screening identifies candidates with high predicted affinity for amyloid-beta. Following synthesis, biological assays are performed to assess the compounds' ability to inhibit aggregation and promote clearance of amyloid plaques in neuronal cell cultures. The iterative analysis using machine learning allows the refinement of the library to enhance the chances of identifying effective neuroprotective agents.

Embodiment 4: Antibiotic Development Against Resistant Bacteria
In this embodiment, the method is employed to discover small molecules targeting multi-drug resistant bacterial strains, such as MRSA (methicillin-resistant Staphylococcus aureus). The pharmacophore model is based on known antibiotics that have demonstrated activity against resistant strains. A library is generated, focusing on compounds with unique structural motifs to overcome resistance mechanisms. Virtual screening prioritizes candidates based on predicted binding to essential bacterial enzymes. The synthesized compounds are then tested for antibacterial activity and selectivity against human cells. Feedback from these assays is used to refine the library iteratively, enabling the discovery of new antibiotics that can effectively combat resistant infections.

Embodiment 5: Cardiovascular Drug Discovery
This embodiment utilizes the method for the identification of small molecules that can lower cholesterol levels. The pharmacophore model is created from existing statins, which are known to inhibit HMG-CoA reductase. A diverse library is generated, focusing on compounds that mimic the structure of statins while introducing novel functionalities to improve potency and reduce side effects. Virtual screening is conducted to identify candidates with high predicted affinities for the enzyme. Synthesized compounds are then evaluated for their ability to lower cholesterol levels in hepatocyte cultures. Machine learning analysis of the data enables further refinement of the compound library, leading to the identification of more effective cholesterol-lowering agents.
, Claims:I/WE CLAIM:
1. A method for the development and screening of small molecule libraries for drug discovery, comprising:
a. constructing a pharmacophore model based on predefined ligand-target interactions;
b. generating a diverse library of small molecules using combinatorial chemistry techniques;
c. conducting virtual screening of the library against selected biological targets to predict binding affinities;
d. synthesizing top-ranking candidates identified from the virtual screening;
e. performing in vitro biological assays on the synthesized candidates to confirm their activity;
f. employing machine learning algorithms to analyze assay results;
Dependent Claim 1:
The method of claim 1, wherein the pharmacophore model is constructed using data from known ligands interacting with the biological target.
Dependent Claim 2:
The method of claim 1, wherein the virtual screening includes molecular docking simulations and scoring functions to assess binding affinities.

Dependent Claim 3:
The method of claim 1, wherein the synthesis of top-ranking candidates is performed using automated robotic synthesis techniques.
Dependent Claim 4:
The method of claim 1, wherein the in vitro biological assays assess pharmacological activity, specificity, and potential toxicity.
Dependent Claim 5:
The method of claim 1, wherein the machine learning algorithms employed are trained on historical datasets of compound activities.
Dependent Claim 6:
The method of claim 1, wherein the feedback loop from biological assays to library refinement is performed at least twice.
Dependent Claim 7:
The method of claim 1, wherein the small molecule libraries generated are applied to therapeutic areas including oncology, infectious diseases, and chronic conditions.

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