pCoMole is a new framework that uses discrete flow matching to guide molecule editing toward user-specified multi-objective preferences while enforcing hard feasibility constraints. It introduces a feasibility-gated terminal distribution with an augmented Tchebycheff utility and implements the preference tilt via a Doob‑h transform, approximated with short Monte Carlo rollouts for efficiency. The method is validated on tasks such as shrinking GFP, shortening Cas9 orthologs, and compressing peptide binders, with wet‑lab tests showing that designed eGFP variants retain fluorescence after extensive edits.
arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...
arXiv:2606. 00555v1 Announce Type: new Abstract: Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives -- binding affinity and druggability -- which single optimization steps rarely improve together.
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
arXiv:2608. 19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability.
arXiv:2606. 01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes.
arXiv:2606. 10543v1 Announce Type: cross Abstract: Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints.
arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.
arXiv:2607. 11978v1 Announce Type: cross Abstract: Precision molecular design aims to discover personalized drug candidates through joint control of multiple conditions, such as biological relevance and molecular design strategies.
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:2607. 09039v1 Announce Type: new Abstract: The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to designability.
The paper proposes a new framework called ensemble-conditioned guidance that reframes molecular design as an optimisation over both the modes and properties of a molecule’s conformational ensemble. It allows 3D generative models to be conditioned simultaneously on multiple axes—such as shapes, pharmacophore profiles, or protein pockets—by adaptively combining vector fields from each condition. The authors introduce adaptive symmetry learning for composable conditions across reference frames, extend the framework to support flexible-size generation, and demonstrate its effectiveness on new benchmarks and practical drug‑discovery tasks, showing improved outcomes when conditioning on additional states compared to single‑state approaches.
Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models from linear to cyclic...