Search · four archives
Search · four archives
12 papers · ranked by Valyu relevance
Brianna Greenstein, Danielle Elsey, Geoffrey Hutchison
Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this work, we examine the general effects of a number of hyperparameters, such as population…
Authors not listed
Deep neural networks have become popular model architectures for fitting coarse-grained molecular dynamics potentials (CGMD) owing to their ability to describe complex features and ease of training against large datasets. However, such architectures are much more complicated than traditional functional forms. This…
Omri Abarbanel, Geoffrey Hutchison
Stable ground-state triplet π-conjugated copolymers have many interesting electronic and optoelectronic properties. However, the large number of potential monomer combinations makes it impractical to synthesize or even just use density functional theory (DFT) to calculate their triplet ground-state stability. Here, we…
Justin Villard, Murat Kılıç, Ursula Rothlisberger
Identification of the most stable structure(s) of a system is a prerequisite for the calculation of any of its properties from first-principles. However, even for relatively small molecules, exhaustive explorations of the potential energy surface (PES) are severely hampered by the dimensionality bottleneck. In this…
Emilie S. Henault, Maria Harris Rasmussen, Jan H. Jensen
We attempt to explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 10 60 molecules) by considering only a tiny subset (typically 10 3−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Authors not listed
More sustainable chemical processes require the selection of suitable molecules, which can be supported by computer-aided molecular design (CAMD). CAMD often generates and evaluates molecular structures using genetic algorithms. However, genetic algorithms can suffer from slow convergence, and might yield suboptimal…
Jan H. Jensen
This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimisation of logP values with a constraint for synthetic accessibility and shows that GA is as good or better than the ML approaches for this particular property. The molecules found by GB-GA bear…
Casper Steinmann, Jan H. Jensen
A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software, starting from randomly chosen molecules from the ZINC database, for four different targets: Bacillus subtilis chorismate mutase (CM), human β 2 -adrenergic G protein-coupled…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Mehmet Cagri Kaymak, Ali Rahnamoun, Kurt A. O'Hearn, Adri C. T. van Duin + 2 more
Molecular dynamics (MD) simulations ease the study of the chemistry of interest. While classical models that governs the interaction of the atoms lack reactivity, the quantum mechanics based methods increase the computational cost drastically. ReaxFF fills the gap between these two ends of the spectrum by allowing bond…