Tango*: Constrained synthesis planning using chemically informed value functions
Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work introduces a specialised bidirectional search algorithm with forward and retro expansion to address the starting material-constrained synthesis problem, allowing CASP systems to provide synthesis pathways from specified starting materials, such as waste products or renewable feed-stocks. In this work, we introduce a simple guided search which allows solving the starting material-constrained synthesis planning problem using an existing, uni-directional search algorithm, Retro*. We show that by optimising a single hyperparameter, Tango* outperforms existing methods in terms of efficiency and solve rate. We find the Tango* cost function catalyses strong improvements for the bidirectional DESP methods. Our method also achieves lower wall clock times while proposing synthetic routes of similar length, a common metric for route quality. Finally, we highlight potential reasons for the strong performance of Tango over neural guided search methods
Code (1)
Similar Papers 제목 키워드 기반
It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design
Constrained synthesizability is an unaddressed challenge in generative molecular design. In particular, designing molecules satisfying multi-parameter optimization objectives, while simultaneously being synthesizable and…
Drug Discoveryreinforcement-learningReinforcement LearningTANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid traversal in cluttered environments requir…
Vision-Language NavigationCoupling Tensor Trains with Graph of Convex Sets: Effective Compression, Exploration, and Planning in the C-Space
We present TANGO (Tensor ANd Graph Optimization), a novel motion planning framework that integrates tensor-based compression with structured graph optimization to enable efficient and scalable trajectory generation. Whil…
Motion PlanningURSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Synthesis planning aiming to find pathways of reactions for a target molecule is one of the most important and challenging tasks in drug discovery. Recent progress has produced both specialized deep-learning retrosynthes…
Drug DiscoveryConstrained Synthesis with Projected Diffusion Models
This paper introduces an approach to endow generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed method recast the traditional sampling proces…
Motion SynthesisVideo Generation