O02_04

Feature Design of Molecular 3D Structures for Fast Approximate Nearest Neighbor Search

Kotaro KAMIYA *

SyntheticGestalt KK
( * E-mail: k.kamiya@syntheticgestalt.com)

Background:
In drug discovery and molecular design, the ability to efficiently compare and search for similar molecular structures is crucial. Machine learning techniques have shown promise in this area, often utilizing 3D representations of molecules that encompass their shape and physicochemical properties, such as charge density and electrostatic potential. However, directly comparing these 3D representations can be computationally expensive, particularly when dealing with large datasets, limiting the scalability of applications like virtual screening.

Challenges and Proposed Solution:
A naive approach to nearest neighbor search, where every molecule is compared to every other molecule, quickly becomes infeasible as the dataset grows. This bottleneck hinders the ability to explore vast chemical spaces efficiently.
To overcome this challenge, we propose leveraging Approximate Nearest Neighbor (ANN) search techniques. ANN algorithms, such as Hierarchical Navigable Small Worlds (HNSW), offer a way to find approximate nearest neighbors with significantly reduced computational cost. These algorithms excel at finding similar items in high-dimensional spaces, making them well-suited for molecular structure comparison.
However, implementing ANN for molecular structures presents two key challenges:
Infinite Dimensions: Molecular properties like electrostatic potential are continuous functions in 3D space, making them inherently infinite-dimensional. ANN algorithms, on the other hand, typically operate on finite-dimensional vector representations.
Alignment Invariance: Meaningful comparisons of molecular structures require considering their alignment in 3D space. A molecule's orientation and position should not affect its similarity to another molecule. Incorporating alignment invariance into ANN search adds another layer of complexity.
To address these challenges, we propose a feature design approach that focuses on creating alignment-invariant representations of molecular structures that are suitable for ANN search. We specifically target 3D molecular graphs, where atoms are represented as nodes and bonds as edges. This representation is both chemically intuitive and computationally efficient.
Our approach involves exploring various techniques for generating alignment-invariant features from 3D molecular graphs. These techniques include persistent homology, which captures topological features of the molecular shape, and a neural network architecture designed for 3D molecular graphs. By extracting and combining these features, we aim to create a compact and informative representation that can be readily used with ANN algorithms.