27 papers · ranked by Valyu relevance
Hannah Earley
The field of molecular programming allows for the programming of the structure and behavior of matter at the molecular level, even to the point of encoding arbitrary computation. However, current approaches tend to be wasteful in terms of monomers, gate complexes, and free energy. In response, we present a novel…
Xiangzhe Kong, Junwei Chen, Ziting Zhang, Gaodeng Li + 24 more
Biomolecular interactions lie at the core of cellular life, spanning diverse molecular modalities from small molecules to nucleic acids and proteins. Nevertheless, design strategies remain separated despite shared physicochemical principles of molecular recognition. Here we present AnewOmni, a unified generative…
Samuel W. Schaffter, Olga B. Vasilyeva, Molly E. Wintenberg, John M. Hurley + 1 more
Programmable cellular information processing could advance biomanufacturing of chemicals and medicine, and enable smart, living therapeutics and diagnostics^1,2^. Nucleic acids circuits based on toehold mediated strand exchange (TMSE) show tremendous potential for cellular programming due to their scalable, composable…
Timothy P. Riley, Mohammad S. Parsa, Pourya Kalantari, Ismail Naderi + 5 more
Traditional protein design is fundamentally constrained by known sequences and folds. To break free from these limitations, we introduce a new alternative: designing proteins directly from plain-language specifications. To achieve this, we trained MP4, a transformer-based model that maps natural language prompts to…
Mustafa Ozen, Ali Abdi, Effat S. Emamian
Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network…
Brian Hie, Salvatore Candido, Zeming Lin, Ori Kabeli + 4 more
Combining a basic set of building blocks into more complex forms is a universal design principle. Most protein designs have proceeded from a manual bottom-up approach using parts created by nature, but top-down design of proteins is fundamentally hard due to biological complexity. We demonstrate how the modularity and…
Joshua Daniel Curry
Most molecular docking tools today rely on stochastic sampling or heuristic optimization. They can be effective, but reproducibility is often hit or miss, and there’s no guarantee the best binding pose has been found. This paper introduces Spatial Needleman-Wunsch, a deterministic dynamic programming framework adapted…
Oliver Goldstein
The Algebraic Data Type (ADT) can be used as a computational framework for molecular representation for the purpose of advancing tasks in cheminformatics. This can include generative modles in the context of Bayesian machine learning via probabilistic programming. The ADT that we put forward, implements the 'Dietz'…
Zhiyuan Yan, Chen Liu, Boxuan Zhao, Kaiqing Lin + 7 more
Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular representation, SMILES, compresses atoms, bonds, branches and rings into a compact sequence in which topology is implicit, forcing LLMs to reconstruct molecular structure…
Thao Nguyen, Heng Ji
We present MolLingo, a multi-agent system that emulates the reasoning process of a chemist to automate molecular design. Existing LLM-based approaches either operate as standalone generative models without access to external tools or lack the multi-agent coordination and shared memory needed for iterative…
Yin Fang, Qiang Zhang, Zhuo Chen, Xiaohui Fan + 1 more
—Machine learning, notably deep learning, has significantly propelled molecular investigations within the biochemical sphere. Traditionally, modeling for such research has centered around a handful of paradigms. For instance, the prediction paradigm is frequently deployed for tasks such as molecular property…
Xin Xia, Yajie Zhang, Xiangxiang Zeng, Xingyi Zhang + 3 more
'Yansen Su' 'Bruno Rizzuti'] Molecular optimization plays a pivotal role in many domains since it holds promise for improving the properties of lead molecules. The advent of artificial intelligence (AI)-driven molecular optimization has revolutionized lead optimization workflows, which have significantly accelerated…
Miles McGibbon, Steven Shave, Jie Dong, Yumiao Gao + 5 more
'Douglas R Houston' 'Jiancong Xie' 'Yuedong Yang' 'Philippe Schwaller' 'Vincent Blay'] Title: Abstract Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To…
Authors not listed
Here, we present MolPic, an open-source Python-based software that can be used to generate high-resolution, publication-quality molecular figures directly from compound names or SMILES strings. MolPic supports single-molecule rendering, batch processing, and automated multi-panel 2D figure generation, which are…
Francois Berenger, Koji Tsuda
Background In recent years, in silico molecular design is regaining interest. To generate on a computer molecules with optimized properties, scoring functions can be coupled with a molecular generator to design novel molecules with a desired property profile. Results In this article, a simple method is described to…
Authors not listed
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges and opportunities of applying generative models to drug discovery…
Debjyoti Bhattacharya, Harrison Cassady, Michael Hickner, Wesley Reinhart
The design of small molecules is crucial for technological applications ranging from drug discovery to energy storage. Due to the vast design space available to modern synthetic chemistry, the community has increasingly sought to use data-driven and machine learning approaches to navigate this space. Although…
David S. Cerutti, Rafal Wiewiora, Simon Boothroyd, Woody Sherman
The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, multicore central processor units (CPUs) and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM…
Hyomin Kim, Yunhui Jang, Sungsoo Ahn
Large language models (LLMs) have large potential for molecular optimization, as they can gather external chemistry tools and enable collaborative interactions to iteratively refine molecular candidates. However, this potential remains underexplored, particularly in the context of structured reasoning…
Alexander S. Shved, Blake E. Ocampo, Elena S. Burlova, Casey L. Olen + 2 more
The construction, management and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries.…
Małgorzata Borówko
Molecular simulation is one of the fastest growing fields in science. This is connected with the rapid increase of computer efficiency and with the appearance of new, sophisticated simulation methods enabling the modeling of complex systems while seamlessly bridging different length and time scales. Now, it is possible…
Authors not listed
In recent years, the development of large language models (LLMs) has revolutionized various fields of natural science, yet their application in molecular data processing remains constrained due to the reliance on single-modality inputs and outputs. To bridge the gap between experimenters and computational tools, we…
Sonny Young
Predicting molecular trajectories is a cornerstone of computational chemistry, with implications for drug discovery and molecular dynamics simulations. This study presents a comprehensive analysis of various machine learning models for the prediction of aspirin molecular trajectories, as captured in a dataset of 1500…
Oliver Lee, Malte Gather, Eli Zysman-Colman
We describe a new tool for the efficient management of computational chemistry. Digichem is a program that automates and simplifies nearly the entire computational pipeline, including large-scale batch submission of calculations, analysis and results parsing, the generation of 3D density plots and 2D graphs of…
Yuri Kochnev, Mayar Ahmed, Alex M Maldonado, Jacob D Durrant
Molecular docking advances early-stage drug discovery by predicting the geometries and affinities of small-molecule compounds bound to drug-target receptors, predictions that researchers can leverage in prioritizing drug candidates for experimental testing. Unfortunately, existing docking tools often suffer from poor…
Mateusz K. Bieniek, Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan + 1 more
Open Source Relative Binding Free Energy Builder with Web Portal Authors: ['Mateusz\nK. Bieniek' 'Alexander D. Wade' 'Agastya P. Bhati' 'Shunzhou Wan' 'Peter V. Coveney'] Relative binding free energy (RBFE) calculations are widely used to aid the process of drug discovery. TIES, Thermodynamic Integration with Enhanced…
Fernanda I. Saldívar-González, Diana L. Prado-Romero, B. Raziel Cedillo-González, Ana L. Chávez-Hernández + 4 more
Searching, retrieving, and analyzing chemical information is one of the main tasks faced by students and professionals in chemistry-related scientific disciplines. Currently, freely available modules developed in programming languages, such as Python, allow efficient data management and facilitate obtaining information…