ML Drug Screening Clustering Targets
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Solution Overview
Problem
The conventional drug discovery process is labor-intensive, time-consuming, and costly, limited by human expertise and focused on known molecules, with a large chemical search space making it intractable to screen candidate molecules against pharmaceutical targets effectively, resulting in low success rates and high development cycles.
Innovation Solution
An automated system using machine learning and artificial intelligence to screen candidate pharmaceutical molecules by clustering pharmaceutical targets based on chemical properties, reducing the search space and identifying likely binding targets, thereby accelerating the drug discovery process and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual screening by human drug experts is used, then domain knowledge and experience can be applied, but the process is labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual visual screening by human drug experts with an automated machine learning system that uses computational algorithms to evaluate molecular structures, predict binding affinities, and identify candidate molecules, thereby eliminating labor-intensive manual processes while maintaining or improving screening accuracy
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously perform screening tasks without continuous human intervention, using trained algorithms to automatically evaluate large numbers of candidate molecules against pharmaceutical targets, thus dramatically increasing productivity while preserving reliable results through algorithmic consistency
2Productivity
If the search space is limited to known molecules, then the screening process is more manageable, but the discovery of novel drug candidates is restricted
Solution Approach 1:
The patent implements a dynamic search space that can adapt between exploring known molecules with established properties and investigating novel molecular structures, allowing the system to flexibly adjust the scope of screening based on project requirements, thereby maintaining efficiency while expanding versatility
Solution Approach 2:
The system extends the search space by incorporating multiple dimensions of molecular evaluation including structural similarity, chemical properties, binding affinity predictions, and pharmacological profiles, enabling comprehensive screening of both known and novel molecules across diverse chemical spaces without compromising screening efficiency
3Reliability
If a large number of candidate molecules are screened, then the success rate of drug discovery increases, but the time and resource costs increase significantly
Solution Approach 1:
The patent applies preliminary action by using machine learning models to pre-evaluate and filter large numbers of candidate molecules before they undergo expensive and time-consuming wet lab experiments, identifying high-priority candidates that are most likely to succeed, thereby increasing the overall success rate while reducing the time and resources required for subsequent experimental validation
Solution Approach 2:
The system performs partial screening by focusing computational resources on evaluating the most promising molecular features and properties using machine learning, rather than exhaustively analyzing every aspect of every candidate molecule, thus achieving high success rate predictions with reduced computational time and resource investment
4Reliability
If conventional screening methods are used, then the process is well-established and reliable, but it cannot effectively handle the large chemical search space of approximately 10^60 candidate molecules
Solution Approach 1:
The patent segments the overwhelming chemical search space into manageable subsets by using machine learning models to identify and prioritize molecular features, structural patterns, and property space regions that are most relevant to drug discovery, thereby making the screening of large molecular spaces tractable while maintaining reliable results through systematic decomposition
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to screen candidate pharmaceutical molecules or compounds for pharmaceutical uses (e.g., treating diseases or conditions). The screening may be based on structural similarity between the candidate pharmaceutical molecules and various pharmaceutical targets (e.g., proteins, nucleic acids, etc.). In aspects, one or more machine learning (ML) models may be trained to assign a candidate pharmaceutical molecule to one of multiple clusters based on chemical/physical properties of the candidate pharmaceutical molecule and chemical/physical properties of pharmaceutical targets associated with the clusters. The pharmaceutical targets associated with the cluster may be scored based on comparisons between the candidate pharmaceutical molecule and the pharmaceutical target, and a subset of the pharmaceutical targets may be identified based on the scores. In some implementations, the subset may be ranked using conjoint analysis and machine learning.