ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the following challenge: can a nontrivial AI model reproduce natural chemical diversity for desired molecules? To illustrate this question, we consider two generative models: a Reinforcement Learning model and the recently introduced ORGAN. Both fail at this challenge. We hope this challenge will stimulate research in this direction.
Code (3)
Tasks
DiversityDrug Discoveryreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative…
Drug DesignDrug DiscoveryInstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex mol…
Drug DiscoveryMolecule CaptioningKEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model
Drug discovery is a critical task in biomedical natural language processing (NLP), yet explainable drug discovery remains underexplored. Meanwhile, large language models (LLMs) have shown remarkable abilities in natural …
Drug DiscoveryKnowledge GraphsLanguage ModelingLanguage Modelling+2A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of …
Molecular Property PredictionDrug DiscoveryGraph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities
Due to their excellent drug-like and pharmacokinetic properties, small molecule drugs are widely used to treat various diseases, making them a critical component of drug discovery. In recent years, with the rapid develop…
Drug DiscoveryOut-of-Distribution Generalization