arXiv subjects

Lukáš Hudec

Publications and source records attributed to Lukáš Hudec.

5 recordsLinked to original sources

arXiv2026

RetroTAC: Learning Route-Derived Synthetic Accessibility for PROTACs

Proteolysis-targeting chimeras (PROTACs) enable targeted protein degradation, expanding therapeutic opportunities beyond conventional inhibition. However, assembling their large, multifunctional structures creates synthetic challenges that hinder their discovery. Traditional synthetic-accessibility scores can overestimate this difficulty by penalizing PROTACs' molecular complexity while overlooking modular assembly from accessible building blocks. In contrast, retrosynthesis planning tools provide explicit routes but are computationally expensive, particularly for large molecules. Here, we introduce a human-interpretable, PROTAC-oriented route-derived score that rewards short synthetic depth and balanced joining of molecular fragments, and RetroTAC, a machine-learning surrogate that predicts this score directly from molecular structures. RetroTAC combines complementary models in an ensemble, achieving a root mean squared error of 0.13 and an $R^2$ of 0.65 on held-out molecules. We further evaluate its utility on PROTACs enumerated by recombining warheads, linkers, and E3-ligase ligands, followed by retrosynthetic planning and route analysis. Candidates selected for high predicted scores achieve a mean route-derived synthesizability of 0.73, compared with 0.31 for low-score candidates; 98.75% versus 51.00% are fully resolved under the tested search settings. These results support RetroTAC as a fast prioritization tool for virtual PROTAC libraries, connecting molecular screening with interpretable route characteristics while reserving explicit planning for selected candidates.

q-bio.QM↗

arXiv2026

The Challenges of PROTAC Permeability Prediction

Cell permeability is a key bottleneck for PROTAC development, and public data available to model it is scarce and inconsistent. We adapt an expert-in-the-loop LLM extraction workflow to mine PAMPA measurements from the primary literature, recovering image-only structures by optical chemical structure recognition and hand-verifying every record, expanding the public record from 31 PROTACs to 87. Ridge models trained on PROTAC-DB 3.0 reach $R^2 = 0.67$ within that resource but collapse on the newly extracted chemistry ($ρ= 0.12$), while models trained on the new compounds transfer back successfully ($ρ= 0.80$). We conclude that the current composition of the published records, and not dataset size, is limiting the construction of more generalizable models, and we outline what would need to change in reporting practices for better data-driven permeability models.

cs.DL↗

arXiv2023

Editable Stain Transformation Of Histological Images Using Unpaired GANs

Double staining in histopathology, particularly for metaplastic breast cancer, typically employs H&E and P63 dyes. However, P63's tissue damage and high cost necessitate alternative methods. This study introduces xAI-CycleGAN, an advanced architecture combining Mask CycleGAN with explainability features and structure-preserving capabilities for transforming H&E stained breast tissue images into P63-like images. The architecture allows for output editing, enhancing resemblance to actual images and enabling further model refinement. We showcase xAI-CycleGAN's efficacy in maintaining structural integrity and generating high-quality images. Additionally, a histopathologist survey indicates the generated images' realism is often comparable to actual images, validating our model's high-quality output.

eess.IV↗

arXiv2023

xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network

In the domain of unsupervised image-to-image transformation using generative transformative models, CycleGAN has become the architecture of choice. One of the primary downsides of this architecture is its relatively slow rate of convergence. In this work, we use discriminator-driven explainability to speed up the convergence rate of the generative model by using saliency maps from the discriminator that mask the gradients of the generator during backpropagation, based on the work of Nagisetty et al., and also introducing the saliency map on input, added onto a Gaussian noise mask, by using an interpretable latent variable based on Wang M.'s Mask CycleGAN. This allows for an explainability fusion in both directions, and utilizing the noise-added saliency map on input as evidence-based counterfactual filtering. This new architecture has much higher rate of convergence than a baseline CycleGAN architecture while preserving the image quality.

cs.CV↗

arXiv2021

Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans

An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based methods are becoming research standard on how to approach 3D scan and image processing tasks, the industry standard for processing this data is still analytically-based. Our paper claims that analytical methods are less robust and harder for testing, updating, and maintaining. This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. Therefore, we present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations. Additionally, we propose two different methods for 6D bin pose estimation, an analytical method as the industrial standard and a baseline data-driven method. Both approaches are cross-evaluated, and our experiments show that augmenting the training on real scans with synthetic data improves our proposed data-driven neural model. This position paper is preliminary, as proposed methods are trained and evaluated on a relatively small initial dataset which we plan to extend in the future.

cs.CV↗