Biology subjects

Quintana, R. A. M.

Publications and source records attributed to Quintana, R. A. M..

8 recordsLinked to original sources

bioRxiv2025

BitBIRCH-Lean: chemical space in the palm of your workstation

We present BitBIRCH-Lean, a fast, memory-efficient implementation of the Bit-BIRCH algorithm, designed for high-throughput clustering of huge molecular libraries (up to billions of drug-like molecules) on typical workstations. BitBIRCH-Lean considerably improves on the original BitBIRCH implementation by incorporating dynamic types and bit-packed fingerprints inside the clustering tree. Most operations in BitBIRCH-Lean are efficiently performed on compressed data, and optional C++ extension accelerate the bottleneck calculations, providing up to 2X speedup. Benchmark tests against GPU-accelerated methods highlight BitBIRCH-Lean as an efficient alternative for processing vast amounts of molecules. We further demonstrate the versatility of this new package by showcasing a parallel, multi-round variant of the Bit- BIRCH algorithm that exploits the gains in efficiency to cluster hundreds of millions of molecules in minutes, with no loss in cluster quality. The code is freely available at: https://github.com/mqcomplab/bblean.

bioinformatics↗

bioRxiv2025

The Shape of Chemical Space

The concept of chemical space is critical in cheminformatics, medicinal chemistry, and machine learning applications. Despite this, the high dimensionality of molecular representations greatly complicates its sampling, analysis, and visualization. A popular approach to overcome problem is to project these representations to a "human-manageable" subspace, usually containing only two dimensions. Non-linear dimensionality reduction techniques are by far the preferred strategy, following the reasoning that their flexibility can accommodate any arbitrary distribution originally present in the high-dimensional space. However, this ignores the elevated computational cost of these methods and the difficulty in tuning their hyper-parameters. Here, we show that basic properties of the metrics used in the original space can be used to infer the shape of the chemical space, which in turns suggests an optimal strategy to project chemical information to lower dimensions. The key insight is to realize that, no matter the set of molecules, their fingerprint representation can be considered to lie on a hyper-spherical surface. The smooth nature of this manifold means that we can use clustering to identify locally-dense sectors of chemical space, and selectively project them simply using linear (hyper-parameter free) methods, like principal component analysis. This approach surpasses non-linear techniques in several neighborhood preservation metrics, while only requiring a fraction of the computational cost. This pipeline is implemented in our N-Ary Mapping Interface (NAMI: https://github.com/mqcomplab/NAMI), which we tested in the visualization of 10 million molecules.

bioinformatics↗

bioRxiv2025

Undersampling techniques for non-linear chemical space visualization

The visualization of high-dimensional chemical space is a critical tool for understanding molecular diversity, structure-property relationships, and for guiding compound selection. However, the performance of non-linear dimensionality reduction (DR) techniques like t-Stochastic Neighborhood Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) are often susceptible to the choice of hyperparameters, along with the high cost of their training for large datasets. In this study, we investigated the effect of undersampling methods on the choice of hyperparameter selection for these non-linear dimensionality reduction methods. Our results demonstrate that selecting small representative subsets of chemical data not only reduces computational costs associated with hyperparameter training but also serves as an innovative means to train non-linear DR methods, leading to projections that better preserve the local structure within the chemical space.

bioinformatics↗

bioRxiv2025

Scaling k-Means for Multi-Million Frames: A Stratified NANI Approach for Large-Scale MD Simulations

We present improved k-means clustering initialization strategies for molecular dynamics (MD) simulations, implemented as part of the N-ary Natural Initiation (NANI) method. Two new deterministic seeding strategies: strat_all and strat_reduced, extend the original NANI approaches and dramatically reduce the clustering runtime while preserving the quality of clustering results. These methods also preserve NANIs reproducible partitioning of well-separated and compact clusters while avoiding the costly iterative seed selection procedures of previous implementations. Testing on the {beta}-heptapeptide and the HP35 systems shows that these new flavors achieved Calinski-Harabasz and Davies-Bouldin scores comparable to the previous NANI variant, indicating that the efficiency gains come with no quality decrease. We also show how these new variants can be used to greatly speed up our previously proposed Hierarchical Extended Linkage Method (HELM). These enhancements extend the reach of NANI to accelerate large-scale MD analysis both in stand-alone k-means clustering and as a component of hybrid workflows, and remove a key barrier to routine, scalable, and reproducible exploration of complex conformational ensembles. The improved NANI implementation is accessible through our MDANCE package: https://github.com/mqcomplab/MDANCE.

biophysics↗

bioRxiv2025

Hierarchical Extended Linkage Method (HELM)'s Deep Dive into Hybrid Clustering Strategies

Clustering remains a key tool in the analysis of molecular dynamics (MD) simulations, from the preparation of kinetic models to the study of mechanistic pathways and structural determination. It is no surprise then that multiple algorithms are currently used in the MD community, with k-means and hierarchical approaches being arguably the two most popular approaches. The former is very attractive from a purely computational point of view, demanding minimal memory and time resources, but at the price of being able to partition the data in very restrictive ways. Hierarchical strategies, on the other hand, can generate arbitrary partitions, but with steep memory and time requirements due to their need to build a pairwise distance matrix for all the considered conformations/frames. Here we propose a new hybrid paradigm, the Hierarchical Extended Linkage Method (HELM), that retains the efficiency of k-means while incorporating the flexibility of hierarchical methods. The key ingredient is the use of n-ary difference functions as a way to stabilize the k-means results and efficiently build the hierarchy of subsets. We showcase the applicability of this strategy over protein-DNA and protein folding studies, including the complete analysis of simulations with over 1.5 million frames. HELM is freely available in our MDANCE clustering package.

biophysics↗

bioRxiv2025

CADENCE: Clustering Algorithm - Density-based Exploration and Novelty Clustering with Efficiency

Unsupervised learning techniques play a pivotal role in unraveling protein folding landscapes, constructing Markov State Models, expediting replica exchange simulations, and discerning drug binding patterns, among other applications. A fundamental challenge in current clustering methods lies in how similarities among objects are accessed. Traditional similarity operations are typically only defined over pairs of objects, and this limitation is at the core of many performance issues. The crux of the problem in this field is that efficient algorithms like k -means struggle to distinguish between metastable states effectively. However, more robust methods like density-based clustering demand substantial computational resources. Extended similarity techniques have been proven to swiftly pinpoint high and low-density regions within the data in linear O(N) time. This offers a highly convenient means to explore complex conformational landscapes, enabling focused exploration of rare events or identification of the most representative conformations, such as the medoid of the dataset. In this contribution, we aim to bridge this gap by introducing a novel density clustering algorithm to the Molecular Dynamics Analysis with N -ary Clustering Ensembles (MDANCE) software package based on n-ary similarity framework.

biophysics↗

bioRxiv2024

iSIM-sigma: efficient standard deviation calculation for molecular similarity

AbstractThe average and variance of the molecular similarities in a set is high-value and useful information for cheminformatics tasks like chemical space exploration and subset selection. However, the calculation of the variance of the complete similarity matrix has a quadratic complexity, O(N2). As the sizes of molecular libraries constantly increase, this pairwise approach is unfeasible. In this work, we present an alternative to obtaining the exact standard deviation of the molecular similarities in a set (with N molecules and M features) for the Russell-Rao (RR) and Sokal-Michener (SM) similarity indexes in O(N M2) complexity. Additionally, we present a highly accurate approximation with linear complexity, O(N), based on the sampling of representative molecules from the set. The proposed approximation can be extended to other similarity indexes, including the popular Jaccard-Tanimoto (JT). With only the sampling of 50 molecules, the proposed method can estimate the standard deviation of the similarities in a set with RMSE lower than 0.01 for sets of up to 50,000 molecules. In comparison, random sampling does not warrant a good approximation as shown in our results.

bioinformatics↗

bioRxiv2024

Protein Retrieval via Integrative Molecular Ensembles (PRIME) through extended similarity indices

Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Processing such ensembles on the basis of statistical mechanics can bring insights about different biologically relevant states, their representative structures, states, and even dynamics between states. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. For some purposes, such as in protein structure prediction, we are interested in identifying the representative structure that is more similar to the native state of the protein. The traditional pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. However, even when the first cluster represents the native basin, the centroid can be several angstroms away in RMSD from the native state - and many other structures inside this cluster could be better choices of representative structures, reducing the need for protein structure refinement. In this study, we developed a module--Protein Retrieval via Integrative Molecular Ensemble (PRIME), that consists of tools to determine the most prevalent states in an ensemble using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N -ary Clustering Ensembles (MDANCE) package and can be used as a post-processing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME was validated with ensembles of different protein and protein complex systems for their ability to reliably identify the most native-like state, which we compare to their experimental structure, and to the traditional approach. Systems were chosen to represent different degrees of difficulty such as folding processes and binding which require large conformational changes. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling - rather than the traditional O(N 2) traditionally associated to comparisons of elements in a set.

biophysics↗