arXiv subjects

Adittya Pal

Publications and source records attributed to Adittya Pal.

5 recordsLinked to original sources

arXiv2026

A Collision-based strategy for Network-free Exploration of Complex Molecular Networks

This work presents a stochastic exploration framework for large, implicitly defined chemical reaction spaces that are too large to be generated and stored as explicit molecular networks. The exploration strategy mimics stochastic chemical kinetics by combining collision-based pair selection with reaction-template instantiation on demand. In each step, the algorithm first samples molecules to collide, then samples a reaction template, and finally samples a concrete reaction instance among the matches of that template. This collision-first factorization avoids exhaustive enumeration of all currently possible reactions and enables exploration of large atomistic reaction spaces under open- or closed-system conditions. We demonstrate the framework on formose chemistry as a case study and analyse both the chemical behaviour reached by the exploration and the computational effects of caching. The implementation is intended as a general tool for exploratory analysis of generative reaction systems.

q-bio.MN↗

arXiv2025

Thermodynamic free energy map for the non-oxidative glycolysis pathways

Designing reaction pathways that maximize the production of a target compound in a given metabolic network is a fundamental problem in systems biology. In this study, we systematically explore the non-oxidative glycolysis metabolic network, guided by the principle that reactions with negative Gibbs free energy differences are thermodynamically favored. We enumerate alternative pathways that implement the net non-oxidative glycolysis reaction, categorized by their length. Our analysis reveals several alternative thermodynamically favorable pathways beyond those reported in experiments. In addition, we identify molecules within the network, such as 3-hydroxypropionic acid, that may have significant potential for further investigation.

q-bio.MN↗

arXiv2025

Finding Pathways in Reaction Networks guided by Energy Barriers using Integer Linear Programming

Analyzing synthesis pathways for target molecules in a chemical reaction network annotated with information on the kinetics of individual reactions is an area of active study. This work presents a computational methodology for searching for pathways in reaction networks which is based on integer linear programming and the modeling of reaction networks by directed hypergraphs. Often multiple pathways fit the given search criteria. To rank them, we develop an objective function based on physical arguments maximizing the probability of the pathway. We furthermore develop an automated pipeline to estimate the energy barriers of individual reactions in reaction networks. Combined, the methodology facilitates flexible and kinetically informed pathway investigations on large reaction networks by computational means, even for networks coming without kinetic annotation, such as those created via generative approaches for expanding molecular spaces. To demonstrate the methodology, we apply it on a chemical reaction network generated from 2-hydroxyethanenitrile, water, and ammonia, where we search for pathways to glycine and 2-hydroxyethanoic acid using the input molecules as precursors.

cs.CE↗

arXiv2024

Finding Thermodynamically Favorable Pathways in Chemical Reaction Networks Using Flows in Hypergraphs and Mixed-Integer Linear Programming

The search for pathways that optimize the formation of a particular target molecule in a reaction network is a key problem in many settings, including reactor systems. Chemical reaction networks are mathematically well represented as hypergraphs, modeling that facilitates the search for pathways by computational means. We propose to enrich an existing search method for pathways by including thermodynamic principles. In more detail, we give a mixed-integer linear programming (mixed ILP) formulation of the search problem into which we integrate chemical potentials and concentrations for individual molecules, enabling us to constrain the search to return pathways containing only thermodynamically favorable reactions. Moreover, if multiple possible pathways are found, we can rank these by objective functions based on thermodynamics. As an example of use, we apply the framework to a reaction network representing the HCN-formamide chemistry. Alternative pathways to the one currently hypothesized in the literature are queried and enumerated, including some that score better according to our chosen objective function.

q-bio.MN↗

arXiv2024

Estimating Reaction Barriers with Deep Reinforcement Learning

Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. We propose solving this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions.

cs.LG↗