Abstract This paper discusses recent optimization approaches to the protein side-chain prediction problem, protein structural alignment, and molecular structure determination from X-ray diffraction measurements. The machinery employed to solve these problems has included algorithms from linear programming, dynamic programming, combinatorial optimization, and mixed-integer nonlinear programming ...

Jan 1, 2020 · In the present study, we propose a novel DP algorithm, COSMO (Codon Optimization Strategy with Multiple Objectives), for solving multicriteria codon optimization problems. This algorithm is deterministic and guarantees that all …

Jul 7, 2015 · We study exact Pareto optimization for two objectives in a dynamic programming framework. We define a binary Pareto product operator on arbitrary scoring schemes.

In this study, we propose a method for optimizing molecular structures based on quantum dynamics computations with working on an FTQC in mind. In our method, the many-body wave functions of nuclei and electrons are directly treated as wavepackets and optimized by the imaginary time evolution method.

May 3, 2004 · In this study, a dynamic programming algorithm for prediction of RNA secondary structure has been revised to use experimentally determined chemical modification constraints. These constraints dramatically improve the accuracy of structure prediction when free energy minimization alone predicts <40% of known base pairs.

Results: This article introduces a systematic method for constructing dynamic programming solutions to problems in biosequence analysis. By a conceptual splitting of the algorithm into a recognition and an evaluation phase, algorithm development is simplified considerably, and correct recurrences can be derived systematically.

Dec 1, 2023 · There exist various RNA folding algorithms that predict an optimal secondary structure as minimum free energy structure of the given RNA sequence in suitable thermodynamic models. In the most frequently used methods, this optimization is performed efficiently by a dynamic programming (DP) algorithm, e.g. mfold [1], RNAfold [2], RNAstructure [3].

To fill this gap, we propose DyMol, the first method designed to tackle the dynamic many-objective molecular optimization problem by utilizing a novel divide-and-conquer approach combined with a decomposition strategy.

Papamichail et al. (2018) study the codon optimization problem when the objectives are CAI and CPB and suggest a polynomial, but an inefficient dynamic programming algorithm to find a design...

Our method uses a reinforcement learning framework that integrates Soft Actor-Critic (SAC) for policy optimization and the Pontryagin Maximum Principle (PMP) to capture molecular dynamics, allowing for continuous control of molecular trajectories.