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Robert Pollice

dblp:286/1696 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 50% Computational science and engineering · 50%
Artificial intelligence
1 paper
Generative modeling · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering
computational chemistry
0.712023
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design · NeurIPS 2023
Computational science and engineering › materials informatics
inverse molecular design
0.712023
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design · NeurIPS 2023
Bioinformatics and computational biology › molecular informatics
molecular design
0.712023
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design · NeurIPS 2023
Bioinformatics and computational biology › drug discovery
molecular optimization
0.712023
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design · NeurIPS 2023
Machine learning › Generative modeling
molecular generation
0.212023
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design · NeurIPS 2023

Methods — techniques the papers use, named apart from their topics

reinforcement learning · 1.3physical simulation · 1.3generative model · 1.3
YearPublicationVenuePosition
2023 Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
abstract
The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelligence development, many algorithms have been developed to tackle this problem. However, despite the emergence of many new approaches in recent years, comparatively little progress has been made in developing realistic benchmarks that reflect the complexity of molecular design for real-world applications. In this work, we develop a set of practical benchmark tasks relying on physical simulation of molecular systems mimicking real-life molecular design problems for materials, drugs, and chemical reactions. Additionally, we demonstrate the utility and ease of use of our new benchmark set by demonstrating how to compare the performance of several well-established families of algorithms. Overall, we believe that our benchmark suite will help move the field towards more realistic molecular design benchmarks, and move the development of inverse molecular design algorithms closer to the practice of designing molecules that solve existing problems in both academia and industry alike.
AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca A. Thiede, Anshul Kundaje, Alán Aspuru-Guzik
NeurIPS2