Biology subjects

Brooks, B. R.

Publications and source records attributed to Brooks, B. R..

7 recordsLinked to original sources

bioRxiv2025

Graph Neural Networks Model Based on Atomic Hybridization for Predicting Drug Targets

Accurate prediction of half-maximal inhibitory concentration (IC50) values is critical for accelerating drug discovery, yet traditional quantitative structure-activity relationship (QSAR) models often have limited ability to capture both local structural patterns and global physicochemical properties essential for bioactivity. We developed a hybrid deep learning framework that integrates graph neural networks with explicit molecular descriptors to address this limitation. The model learns from molecular graphs encoding atomic and bond features while incorporating interpretable physicochemical properties and structural fingerprints. Trained and validated on 14,316 compounds across nine diverse biological targets including kinases, nuclear receptors, and proteases, our approach achieved an overall test R2 of 0.87, consistently outperforming previously reported methods by 6-42% across evaluated targets. The model demonstrated robust generalization with near-identical training and test performance, while maintaining partial interpretability through transparent descriptor contributions and attention mechanisms. By synergistically combining data-driven learning with domain knowledge, this hybrid framework offers improved accuracy and interpretability for structure-activity modeling, facilitating more efficient compound prioritization and optimization in early-stage drug discovery programs.

bioinformatics↗

bioRxiv2025

Machine Learning-Driven Drug Repurposing for KRAS G12C and KRAS G12D Inhibition

KRAS is a predominant oncogenic driver across multiple cancers, long considered "undruggable" due to its high nucleotide affinity and lack of classical binding pockets. Although recent advances have led to covalent inhibitors like Sotorasib and Adagrasib for the KRAS G12C mutation, effective therapies for other common variants--most notably KRAS G12D, which is highly prevalent in aggressive pancreatic cancers--remain limited. In this study, we employ machine learning to identify potential inhibitors for both KRAS G12D and G12C by screening FDA-approved compounds from the ChEMBL database. Random Forest and Neural Network models were trained on binding affinity data from three BindingDB datasets: wild-type KRAS GTPase, KRAS G12C, and KRAS G12D. Our models identified high-affinity candidates including anti-cancer kinase inhibitors (e.g., Cobimetinib, Gilteritinib) as well as drugs from other categories (e.g., Bromocriptine, Cefepime). By incorporating atomic hybridization as a feature, we aim to capture the effects of induced polarization, potentially improving prediction accuracy. Despite modest agreement across models, our approach highlights several promising candidates--such as Acalabrutinib, which has prior evidence of G12C activity--for further investigation. These results offer a data-driven foundation for experimental validation and the future development of targeted KRAS therapies.

cancer biology↗

bioRxiv2025

The role of induced polarization in drug discovery applications

Induced polarization plays a pivotal role in ligand-protein binding by enhancing both the specificity and strength of molecular interactions. As a ligand approaches a protein, their respective electronic clouds redistribute in response to each others electrostatic fields--a phenomenon governed by induced polarization. The response of a molecules electron density to an external field is quantitatively described by its polarizability tensor. In this study, we calculated polarizability tensors for thousands of drug-like molecules from the CHEMBL database, focusing on compounds targeting the Thrombin, Estrogen Receptor alpha, and Phosphodiesterase 5A proteins using Density Functional Theory (DFT). We show that a machine learning model based on atomic hybridization accurately predict the polarizabilities eigenvalues, calculated with DFT. Then, we build a neural network and random forest models to predict the IC50s based on the same features. The success of these models, despite utilizing a limited number of features, underscores the critical role of induced polarizabilities in determining binding energies.

bioinformatics↗

bioRxiv2022

Deep attention based variational autoencoder for antimicrobial peptide discovery

Antimicrobial peptides (AMPs) have been proposed as a potential solution against multiresistant pathogens. Designing novel AMPs requires exploration of a vast chemical space which makes it a challenging problem. Recently natural language processing and generative deep learning have shown great promise in exploring the vast chemical space and generating new chemicals with desired properties. In this study we leverage a variational attention mechanism in the generative variational autoencoder where attention vector is also modeled as a latent vector. Variational attention helps with the diversity and quality of the generated AMPs. The generated AMPs from this model are novel, have high statistical fidelity and have similar physicochemical properties such as charge, hydrophobicity and hydrophobic moment to the real to the real antimicrobial peptides.

bioinformatics↗

bioRxiv2020

Exploring dynamics and network analysis of spike glycoprotein of SARS-COV-2

The ongoing pandemic caused by coronavirus SARS-COV-2 continues to rage with devastating consequences on human health and global economy. The spike glycoprotein on the surface of coronavirus mediates its entry into host cells and is the target of all current antibody design efforts to neutralize the virus. The glycan shield of the spike helps the virus to evade the human immune response by providing a thick sugar-coated barrier against any antibody. To study the dynamic motion of glycans in the spike protein, we performed microsecond-long MD simulation in two different states that correspond to the receptor binding domain in open or closed conformations. Analysis of this microsecond-long simulation revealed a scissoring motion on the N-terminal domain of neighboring monomers in the spike trimer. Role of multiple glycans in shielding of spike protein in different regions were uncovered by a network analysis, where the high betweenness centrality of glycans at the apex revealed their importance and function in the glycan shield. Microdomains of glycans were identified featuring a high degree of intra-communication in these microdomains. An antibody overlap analysis revealed the glycan microdomains as well as individual glycans that inhibit access to the antibody epitopes on the spike protein. Overall, the results of this study provide detailed understanding of the spike glycan shield, which may be utilized for therapeutic efforts against this crisis.

biophysics↗

bioRxiv2020

Critical Sequence Hot-spots for Binding of nCOV-2019 to ACE2 as Evaluated by Molecular Simulations

The novel coronavirus (nCOV-2019) outbreak has put the world on edge, causing millions of cases and hundreds of thousands of deaths all around the world, as of June 2020, let alone the societal and economic impacts of the crisis. The spike protein of nCOV-2019 resides on the virions surface mediating coronavirus entry into host cells by binding its receptor binding domain (RBD) to the host cell surface receptor protein, angiotensin converter enzyme (ACE2). Our goal is to provide a detailed structural mechanism of how nCOV-2019 recognizes and establishes contacts with ACE2 and its difference with an earlier coronavirus SARS-COV in 2002 via extensive molecular dynamics (MD) simulations. Numerous mutations have been identified in the RBD of nCOV-2019 strains isolated from humans in different parts of the world. In this study, we investigated the effect of these mutations as well as other Ala-scanning mutations on the stability of RBD/ACE2 complex. It is found that most of the naturally-occurring mutations to the RBD either strengthen or have the same binding affinity to ACE2 as the wild-type nCOV-2019. This may have implications for high human-to-human transmission of coronavirus in regions where these mutations have been found as well as any vaccine design endeavors since these mutations could act as antibody escape mutants. Furthermore, in-silico Ala-scanning and long-timescale MD simulations, highlight the crucial role of the residues at the interface of RBD and ACE2 that may be used as potential pharmacophores for any drug development endeavors. From an evolutional perspective, this study also identifies how the virus has evolved from its predecessor SARS-COV and how it could further evolve to become more infectious.

biophysics↗

bioRxiv2016

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

biophysics↗