ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2410.10431
  4. Cited By
Diversity-Aware Reinforcement Learning for de novo Drug Design
v1v2 (latest)

Diversity-Aware Reinforcement Learning for de novo Drug Design

International Joint Conference on Artificial Intelligence (IJCAI), 2024
14 October 2024
Hampus Gummesson Svensson
C. Tyrchan
Ola Engkvist
M. Chehreghani
ArXiv (abs)PDFHTML

Papers citing "Diversity-Aware Reinforcement Learning for de novo Drug Design"

11 / 11 papers shown
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design
Hampus Gummesson Svensson
Ola Engkvist
J. Janet
C. Tyrchan
M. Chehreghani
OffRL
325
0
0
26 Jun 2025
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic
  Rewards for Goal-directed Molecular Generation
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation
Jinyeong Park
Jaegyoon Ahn
Jonghwan Choi
Jibum Kim
210
9
0
29 Mar 2024
Utilizing Reinforcement Learning for de novo Drug Design
Utilizing Reinforcement Learning for de novo Drug DesignMachine-mediated learning (ML), 2023
Hampus Gummesson Svensson
C. Tyrchan
Ola Engkvist
M. Chehreghani
318
28
0
30 Mar 2023
Re-evaluating sample efficiency in de novo molecule generation
Re-evaluating sample efficiency in de novo molecule generation
Morgan Thomas
Noel M. O'Boyle
Andreas Bender
Chris De Graaf
48
11
0
01 Dec 2022
Never Give Up: Learning Directed Exploration Strategies
Never Give Up: Learning Directed Exploration StrategiesInternational Conference on Learning Representations (ICLR), 2020
Adria Puigdomenech Badia
Pablo Sprechmann
Alex Vitvitskyi
Daniel Guo
Bilal Piot
...
O. Tieleman
Martín Arjovsky
Alexander Pritzel
Andew Bolt
Charles Blundell
198
339
0
14 Feb 2020
Exploration by Random Network Distillation
Exploration by Random Network Distillation
Yuri Burda
Harrison Edwards
Amos Storkey
Oleg Klimov
283
1,518
0
30 Oct 2018
Deep Reinforcement Learning for De-Novo Drug Design
Deep Reinforcement Learning for De-Novo Drug Design
Mariya Popova
Olexandr Isayev
Alexander Tropsha
271
1,139
0
29 Nov 2017
Molecular De Novo Design through Deep Reinforcement Learning
Molecular De Novo Design through Deep Reinforcement Learning
Marcus Olivecrona
T. Blaschke
Ola Engkvist
Hongming Chen
BDL
416
1,136
0
25 Apr 2017
Unifying Count-Based Exploration and Intrinsic Motivation
Unifying Count-Based Exploration and Intrinsic MotivationNeural Information Processing Systems (NeurIPS), 2016
Marc G. Bellemare
S. Srinivasan
Georg Ostrovski
Tom Schaul
D. Saxton
Rémi Munos
356
1,586
0
06 Jun 2016
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic OptimizationInternational Conference on Learning Representations (ICLR), 2014
Diederik P. Kingma
Jimmy Ba
ODL
4.7K
161,218
0
22 Dec 2014
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond
The KL-UCB Algorithm for Bounded Stochastic Bandits and BeyondAnnual Conference Computational Learning Theory (COLT), 2011
Aurélien Garivier
Olivier Cappé
616
638
0
12 Feb 2011
1