Discover the Latest Advances in Generative AI Research: An In-depth Analysis
We have recently found this insightful post from Recursion 's Ali Madani highlighting cutting-edge research in the field of generative AI:
It is truly remarkable to witness the progress made by researchers from companies such as iktos , Nostrum Biodiscovery , Nxera Pharma ?all well as others from esteemed Universities from all over the globe. Their studies delve into critical areas and problem points of todays generative AI, including synthesizability, gene identification tools for target discovery, low prediction accuracy etc.?
At ANYO Labs, we find the problems addressed in these research papers both inspiring and informative, as they align with our own developments.?
Filella-Merce, et al. (1) tackles significant challenges associated with generative limitations, such as low affinity towards the target, unknown ADME/PK properties, and limited synthetic tractability. Fortunately, at ANYO Labs, we overcome these limitations by delivering potent molecules with optimal predicted ADMET properties and synthetic tractability early in the drug discovery process for all our generated de novo compounds, all with exceptional computational efficiency and precision.?
Accuracy and reproducibility are fundamental principles guiding our models. Several articles from renowned universities showcase remarkable progress in addressing the issue of low accuracy in binding affinity predictions (2, 3, 4, 5, 6, 7). These studies introduce novel advancements on methods in graph structure design, graph convolution networks, and functional group-based diffusion models to name a few. By improving accuracy while ensuring reproducibility, these advancements are crucial steps toward reducing downstream attrition rates resulting from poor ligand selection.
Thomas, et al. (8) skilfully shed light on the limitations of current machine learning (ML) models. They rightly emphasize that although virtual screening commonly employs structure-based drug design principles when available, the majority of generative molecular design approaches have predominantly focused on de novo generation using ligand-based drug design approaches.
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Through our novel ML approaches, ANYO Labs has overcome these limitations by leveraging prior knowledge to transcend the constraints of traditional approaches. This has enabled us to produce outstanding results that outperform competitors in the CSAR and CASF benchmark parameters (Figure 1).
The paper also raises a valid concern regarding the synthesizability of generated molecules, which may not always be guaranteed. This issue was addressed by researchers at Iktos (9). Parrot, et al. adeptly highlights the importance of considering synthesizability during molecule generation, as it is a fundamental requirement for these methods to be practical and useful. At ANYO Labs, we share this viewpoint, and we integrate synthesizability considerations into our output data generation process using our proprietary tools and medicinal expertise.
ANYO Labs' unique approach to the generative AI problem stems from years of research dedicated to streamlining and comprehending the chemistry underlying generative models. Our approach, designed by chemists for chemists, has yielded exceptional results.
We eagerly anticipate contributing our own articles to this ever-evolving field. Please stay tuned for our upcoming publications as we aspire to stand alongside these remarkable referenced companies.