arXiv Machine Learning

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

arXiv:2607. 12349v1 Announce Type: new Abstract: Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design.

arXiv Machine Learning
Jul 2

SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles

arXiv:2607. 01105v1 Announce Type: new Abstract: We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to make it).

By Miruna Cretu, John Bradshaw, Patricia Suriana, Saeed Saremi, Omar Mahmood, Kirill Shmilovich, Kangway Chuang, Vishnu Sresht, Colin Grambow
arXiv AI
Jul 21

Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints

arXiv:2607. 18144v1 Announce Type: cross Abstract: Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules.

By Thomas MacDougall, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Maxim Malkov, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov
arXiv AI
Jul 10

DrugGen 2: A disease-aware language model for enhancing drug discovery

arXiv:2607. 08404v1 Announce Type: cross Abstract: Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes.

By Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami, Navid Mazrouei, Matin Irajpour, Yousof Gheisari, Hajar Sirous
arXiv AI
Sep 21

SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity

SpecOpt is a new molecular design task that optimizes the binding specificity of existing drugs by making constrained structural modifications. The method uses an agentic framework that docks a compound against its intended target and known off‑targets, compares residue‑aware atom‑protein contacts, and feeds the differential interactions to a large language model to propose changes. On a benchmark of 915 compounds, SpecOpt increased the target‑off‑target binding gap for 84.8% of cases while preserving drug‑like properties and structural similarity.

By Thao Nguyen, Heng Ji
arXiv Machine Learning
Sep 10

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

PocketVE is a protein-pocket-conditioned variance‑exploding diffusion framework that integrates stable 3D coordinate denoising, classifier‑free property guidance, and adaptive protein perturbation. It improves 3D validity from 58.6% to 80.6% and reduces strain energy from 457.4 to 127.9 on CrossDocked2020 while maintaining competitive docking and property scores. The study shows moderate guidance balances target objectives with geometric quality, and diagnostics confirm enhanced pocket compatibility.

By Peining Zhang, Jinbo Bi
arXiv Machine Learning
Sep 10

FuseDiff: Symmetry-Preserving Joint Diffusion for Dual-Target Structure-Based Drug Design

FuseDiff is an end‑to‑end diffusion model designed for dual‑target structure‑based drug design, jointly generating a ligand graph and two pocket‑specific binding poses conditioned on both target pockets. It employs a message‑passing backbone with Dual‑target Local Context Fusion (DLCF) to fuse ligand atom contexts from both pockets, preserving symmetry while enabling expressive joint modeling. The model enforces topological consistency across the two poses and allows target‑specific geometric adaptation, achieving state‑of‑the‑art docking performance and enabling systematic assessment of dual‑target pose quality before docking‑based pose search.

By Jianliang Wu, Anjie Qiao, Zhen Wang, Zhewei Wei, Sheng Chen
arXiv AI
Jul 24

Beyond SBDD: Geometric Deep Learning in Polypharmacology and Multi-target Drug Design

arXiv:2607. 20550v1 Announce Type: cross Abstract: The traditional "one drug, one target" paradigm of structure-based drug design (SBDD) frequently proves inadequate for treating multifactorial diseases such as cancer and neurodegenerative disorders, owing to compensatory signaling pathways and the emergence of drug resistance.

By Tianming Han, Zhijie Pan, Wenchi Ge, Qi Zhao