Biology subjects

Elizarova, E.

Publications and source records attributed to Elizarova, E..

4 recordsLinked to original sources

bioRxiv2026

A Unified 3D Generative Model for Synthesizable Structure-Based Drug Design

Traditional screening-based drug discovery is inherently limited by the astronomical scale of the chemical space. Generative modelling offers a compelling alternative to the classical search paradigm and enables rational, bottom-up design of novel and target-specific small molecules. However, its impact has been hampered by challenges in synthetic accessibility of the designed compounds and lack of large-scale experimental validation. Here, we introduce LDDM (Large Drug Discovery Model), a generative framework that supports a range of drug discovery tasks, including constrained and unconstrained docking, fragment linking and growing, and de novo design. We further introduce a programmable design algorithm that enables accurate design of synthetically accessible compounds satisfying various fine-grained objectives. We experimentally validated the designed or optimised ligands for five therapeutically relevant protein targets. In all cases, LDDM achieved high success rates, allowing us to identify molecules with confirmed binding affinity while synthesizing only a small number of generated compounds. The best designs were structurally characterised through NMR spectroscopy and X-ray crystallography, demonstrating high prediction accuracy. Overall, LDDM provides a scalable and flexible platform for the rapid and tailored design of small molecules and non-natural peptides for therapeutic applications.

bioinformatics↗

bioRxiv2026

The Human Bindome: A Proteome-scale Atlas of Designed Binder Candidates

Affinity reagents such as antibodies are indispensable for interrogating proteins biological function. Yet they are costly and frequently unreliable, with unknown sequences, posing challenges to reproducible experimental research. Deep learning-based protein design can now in silico generate affinity reagents achieving reliable experimental success rates, but has remained largely confined to specialist laboratories. Here we present the Human Bindome, a proteome-scale atlas of high-confidence in silico protein binder candidates. By embedding the experimentally benchmarked BindCraft method in an accelerated, parallelized framework with automated domain-level target selection, we generated 306,146 binder candidates covering 8,296 human proteins (40.9% of the full proteome). Every candidate carries a defined sequence, a predicted binder-target structure model, and in silico confidence metrics. We characterize proteome-wide coverage and show that binder epitopes frequently overlap functional sites. This positions the Bindome as a resource of genetically encodable perturbagens for site-specific, modular control of protein function. The Bindome is freely available through a web interface (https://bindome.epfl.ch), with agentic, natural-language querying and as data splits for machine-learning model development. We anticipate that the Bindome will be valuable for the scientific community by providing affinity and perturbation reagents with broad applications in dissecting biological mechanisms as well as in drug and target discovery.

synthetic biology↗

bioRxiv2025

FragmentScope - exploring the fragment space with learned surface representations

Exploring fragment chemical space for ligand design remains a major challenge in early stage drug discovery. This task is particularly challenging due to the small size, low specificity, and weak binding affinities of low molecular weight (MW) fragments. We present FragmentScope, a computational pipeline that uses learned protein surface fingerprints to guide fragment placement and small molecule generation. By using a contrastive learning model trained on protein-ligand interactions, we built a database of surface-fragment pairs, which enables fast and accurate fragment placement in the target protein pocket. We demonstrate its effectiveness on benchmark datasets, achieving robust placement accuracy. FragmentScope also enables the design of small molecules based on the predicted fragments ensuring synthetic accessibility. We experimentally validated Fragmentscope across 5 different targets with binding assays and structural characterization. Our approach shows high success rates in fragment discovery and yielded promising leads for designed ligands. FragmentScope offers a scalable, structure-guided approach for narrowing chemical space and identifying prospective scaffolds, accelerating the early stages of small molecule design.

bioinformatics↗

bioRxiv2024

Targeting protein-ligand neosurfaces using a generalizable deep learning approach

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, where protein-protein interactions are conditioned to small molecules. Here, we present a computational strategy for the design of proteins that target neosurfaces, i.e. surfaces arising from protein-ligand complexes. To do so, we leveraged a deep learning approach based on learned molecular surface representations and experimentally validated binders against three drug-bound protein complexes. Remarkably, surface fingerprints trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in deep learning approaches. The designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells.

biochemistry↗