At HotSpot, we’re harnessing insights from nature by pinpointing specialized protein on/off switches, or pockets, called “natural hotspots.” To date, we’ve successfully identified approximately 1,500 proteins that possess these natural hotspot mechanisms governing a protein’s cellular functions.
In a previous blog entry, we delved into the significant role of artificial intelligence (AI). Pattern recognition stands out as a core component of AI, as it empowers systems to recognize, interpret, and react to patterns and consistencies in data.

As we continue to evolve our Smart Allostery™ platform, we recognized a fundamental analogy when scrutinizing these naturally occurring pockets; they exhibit certain characteristics implicative of recurring patterns, akin to repeatable images with special characteristics. The primary challenge, however, resides in the precise identification of these patterns within extensive and intricate deep datasets. To address this challenge, we took lessons from facial recognition, a specific subset of pattern recognition. By deploying principles akin to facial recognition patterns such as eyes, nose, mouth, and overall facial structure to locate the “face” of natural hotspots, we aim to bridge the gap between the visual typology discernible to a seasoned scientist and the potent and high throughput capabilities of machine learning for drug discovery.

Given that the applications and the technologies supporting accelerated pattern recognition are evolving rapidly, we thought it would be helpful to shed light on the significance of pattern recognition in drug discovery and its role in potentially solving complex biological problems. We will explore the essential ingredients necessary for successful pattern recognition, emphasizing the importance of bringing together the right visual data coupled with the right human drug hunter’s insights and the right algorithms.

Cracking the Code for Successful Pattern Recognition

We believe that solving complex problems with data insights and predictions requires the following:

  1. A Good Problem: The first ingredient for successful pattern recognition is to define a good problem. This involves identifying a task or challenge that can benefit from pattern recognition techniques. Whether it’s image or speech recognition, fraud detection, or predicting customer behavior, a well-defined problem lays the foundation for effective pattern recognition. Success in drug discovery is critically dependent on selecting the right target biology and how to interrogate that biology. This approach recognizes that proteins have regions beyond their primary functional sites that can be strategically targeted to achieve desired therapeutic outcomes.

For HotSpot, our primary focus is to systematically deliver first and only allosteric small molecules across multiple target classes relevant to oncology and immunology.  A crucial element requires gaining a deeper understanding of the intricate mechanisms that underlie these diseases and their associated biological pathways. We believe that by comprehending the holistic functionality of potential drug targets within these pathways, as well as understanding the pathways’ roles in the context of the disease they affect, will ultimately pave the way for groundbreaking approaches to drug discovery.

  • A Good (and Large) Data Set: Data is the lifeblood of pattern recognition: A substantial and diverse data set is essential to extract meaningful patterns. The data set should accurately represent the problem domain, capturing the variations and nuances that need recognition and understanding. The availability of a large and high-quality data set enables the model to learn robust patterns and generalize well to new data. Datasets to address these problems are plentiful, spanning mutations, genomic sequencing, protein structure and small molecules. With that said, the challenges of collecting and cleaning such data should not be underestimated, due to its scattered sources and various formats. Fortunately, the good news is that machine learning is making significant advancements in handling noisy data and extracting valuable information from diverse data types.

We have applied these key learnings and created a comprehensive database inside the SpotFinder component of our platform. It is meticulously curated to incorporate the essential data required for the application of large-scale machine learning and pattern recognition algorithms. The data are gathered from widely used publicly accessible databases, as well as information extracted from scientific literature through advanced natural language processing techniques.

  • A Key Insight into the Problem: Insight plays a vital role in pattern recognition. It involves understanding the problem domain, identifying relevant features, and discerning the underlying patterns that lead to accurate predictions or decisions. This insight could come from domain expertise, exploratory data analysis, or prior research. Having a deep understanding of the problem enables the development of effective algorithms that capture the relevant patterns.   This is the point of interaction between machine learning/pattern recognition.  
  • The Right Algorithm to Interpret the Data: Selecting the appropriate algorithm is crucial for pattern recognition. Different algorithms, such as decision trees, support vector machines, neural networks, or clustering methods, have varying strengths and limitations. The choice of algorithm depends on the problem at hand, the nature of the data, and the desired outcomes. The selected algorithm should be capable of capturing and interpreting the patterns identified through insights, leading to accurate predictions or classifications.

Post Hoc Thought

Recognizing that drug development is highly complex and multifaceted, pattern recognition can serve as a fundamental and robust approach to solving complex problems. It encompasses the capability of machines to detect patterns within data and subsequently employ these patterns to inform decision-making or predictions through computer algorithms. This function is an indispensable component of modern AI systems.

By focusing on a well-defined problem, a good data set, key insights, and the right algorithm, scientists can unlock the potential of pattern recognition and make significant strides in solving real-world clinical challenges.