BEIJING, May 22, 2024
/PRNewswire/ -- Docking is crucial in the early stages of drug
discovery as it enables the efficient screening of vast libraries
of compounds, saving time and resources. In recent years, AI-based
methods have emerged as promising alternatives for molecular
docking, offering the potential for high accuracy without incurring
prohibitive computational costs. Advancing AI for Science, DP
Technology has recently open-sourced its powerful
AI-based Uni-Mol Docking v2 model [1] and made it
available to the science community. Uni-Mol Docking v2 is a
powerful docking model released prior to AlphaFold3
[2].
AI assisted Drug Discovery
Docking is a computational technique used in drug discovery to
predict how "well" a small molecule (drug candidate) interacts with
a target protein. Docking is crucial in the early stages of drug
discovery as it enables the efficient screening of vast libraries
of compounds, saving time and resources. Globally, the molecular
docking market is a significant segment within the broader drug
discovery informatics market, which is projected to grow from
USD 3 billion in 2023 to USD 8 billion by 2032, driven by increasing
investments in R&D and the adoption of advanced computational
tools by pharmaceutical companies [3].
Docking techniques have evolved significantly from traditional
physics/scoring-based methods to advanced deep learning / AI-based
approaches. Initially, docking relied on physical and chemical
principles to predict interactions, using scoring functions to
evaluate binding affinities based on geometric and energetic
considerations. While effective, these methods were computationally
intensive and limited by their reliance on predefined rules. The
advent of deep learning has transformed docking by enabling models
to learn complex molecular representations directly from data.
These deep learning models can capture intricate patterns and
interactions that were previously hard to model, leading to more
accurate and efficient predictions.
Uni-Mol Docking v2, is based on pre-trained AI model developed
by DP Technology. The Uni-Mol modeling series, published at ICLR
2023 [4], described the pretraining of general molecular
encoders and showcased their applications in various 2D and 3D
downstream tasks such as molecular conformation generation,
molecular property prediction and molecular docking. Uni-Mol
achieved state-of-the-art results on a wide range of tasks,
indicating its competence for generalization, and making it a
powerful foundational model for molecular tasks.
In molecular docking, leveraging a pretrained molecular encoder,
pretrained pocket encoder and joint pocket-ligand blocks, Uni-Mol
Docking v2 achieved superior performance when compared to
traditional docking algorithms like Autodock Vina in the CASF-2016
benchmark. Uni-Mol Docking v2 boasts an improved accuracy in
predicting these binding poses with over 77% of ligands achieving
an RMSD value under 2.0 Å and over 75% passing all quality checks.
This marks a substantial leap from the 62% accuracy of our previous
version, also eclipsing other known open-sourced methods. We've
effectively tackled common challenges like chirality inversions and
steric clashes, ensuring our predictions are not just accurate but
also chemically viable.
Recently, the introduction of AlphaFold3 has been widely
discussed in the scientific community. In this latest development,
AlphaFold3 extends its capabilities to predict protein-ligand
docking poses. In the AlphaFold3 paper on Nature, Uni-Mol Docking
v2 is featured as a benchmark [2].
Committing to open science, DP Technology is proud to
open-source Uni-Mol Docking v2 model, code, and dataset, making
them available to the science community. We will continue to work
on future iterations of Uni-Mol Docking and beyond as we commit to
contributing to the global scientific community.
DP Technology is dedicated to advancing the frontiers of science
through our unwavering commitment to open science. By openly
sharing the research, data, and tools, DP aims to empower the
global scientific community, accelerate discovery, and drive
meaningful progress across various fields. DP's commitment to
transparency and collaboration is rooted in the conviction that the
best solutions to the world's challenges emerge when we work
together, breaking down barriers to knowledge and fostering a
culture of collective advancement.
Guided by this value, DP has been contributing actively and
significantly to many open science projects in material science,
drug discovery etc. They can be found at https://deepmodeling.com/.
Recently, DP Technology is proud to join DeepModeling community in
launching OpenLAM, towards developing the first Large Atom Model
https://www.aissquare.com/openlam. DP has collaborated with global
institutions to release DPA-2, which can accurately represent a
diverse range of chemical systems and materials, enabling
high-quality simulations and predictions with significantly reduced
efforts compared to traditional methods[5].
Access Uni-Mol Docking v2
Ready-to-use Webapp:
https://bohrium.dp.tech/apps/unimoldockingv2
Github:
https://github.com/dptech-corp/Uni-Mol/tree/main/unimol_docking_v2
Paper: https://arxiv.org/abs/2405.11769
About DP Technology
DP Technology is a global leader in the "AI for Science"
research paradigm, where AI learns scientific principles and data,
then tackles key challenges in scientific research and industrial
R&D.
DP's commitment to interdisciplinary research has led to the
creation of the DP "Particle Universe", an array of pre-trained
large science models designed to bridge foundational research with
practical industrial applications. DP's software suite
includes the Bohrium® Scientific Research Space, Hermite®
Computational Drug Design Platform, RiDYMO® Dynamics Platform, and
Piloteye® Battery Design Automation Platform. Together, these
platforms form a robust foundation for industrial innovation and an
open ecosystem for AI in science, fostering advancements in key
areas such as drug discovery, energy, material science, and
information technology.
Visit DP Technology at www.dp.tech/en
Reference
|
[1]
https://arxiv.org/abs/2405.11769
|
[2]
https://www.nature.com/articles/s41586-024-07487-w
|
[3]
https://www.grandviewresearch.com/industry-analysis/drug-discovery-informatics-market
;
https://www.alliedmarketresearch.com/drug-discovery-informatics-market-A07074;
|
[4]
https://openreview.net/forum?id=6K2RM6wVqKu
|
[5]
https://arxiv.org/abs/2312.15492
|
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SOURCE DP Technology