125 research outputs found
Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?
Molecular conformation generation (MCG) is a fundamental and important
problem in drug discovery. Many traditional methods have been developed to
solve the MCG problem, such as systematic searching, model-building, random
searching, distance geometry, molecular dynamics, Monte Carlo methods, etc.
However, they have some limitations depending on the molecular structures.
Recently, there are plenty of deep learning based MCG methods, which claim they
largely outperform the traditional methods. However, to our surprise, we design
a simple and cheap algorithm (parameter-free) based on the traditional methods
and find it is comparable to or even outperforms deep learning based MCG
methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks. In particular,
our design algorithm is simply the clustering of the RDKIT-generated
conformations. We hope our findings can help the community to revise the deep
learning methods for MCG. The code of the proposed algorithm could be found at
https://gist.github.com/ZhouGengmo/5b565f51adafcd911c0bc115b2ef027c
3D Molecular Generation via Virtual Dynamics
Structure-based drug design, i.e., finding molecules with high affinities to
the target protein pocket, is one of the most critical tasks in drug discovery.
Traditional solutions, like virtual screening, require exhaustively searching
on a large molecular database, which are inefficient and cannot return novel
molecules beyond the database. The pocket-based 3D molecular generation model,
i.e., directly generating a molecule with a 3D structure and binding position
in the pocket, is a new promising way to address this issue. Herein, we propose
VD-Gen, a novel pocket-based 3D molecular generation pipeline. VD-Gen consists
of several carefully designed stages to generate fine-grained 3D molecules with
binding positions in the pocket cavity end-to-end. Rather than directly
generating or sampling atoms with 3D positions in the pocket like in early
attempts, in VD-Gen, we first randomly initialize many virtual particles in the
pocket; then iteratively move these virtual particles, making the distribution
of virtual particles approximate the distribution of molecular atoms. After
virtual particles are stabilized in 3D space, we extract a 3D molecule from
them. Finally, we further refine atoms in the extracted molecule by iterative
movement again, to get a high-quality 3D molecule, and predict a confidence
score for it. Extensive experiment results on pocket-based molecular generation
demonstrate that VD-Gen can generate novel 3D molecules to fill the target
pocket cavity with high binding affinities, significantly outperforming
previous baselines
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
Low-resource automatic speech recognition (ASR) is challenging, as the
low-resource target language data cannot well train an ASR model. To solve this
issue, meta-learning formulates ASR for each source language into many small
ASR tasks and meta-learns a model initialization on all tasks from different
source languages to access fast adaptation on unseen target languages. However,
for different source languages, the quantity and difficulty vary greatly
because of their different data scales and diverse phonological systems, which
leads to task-quantity and task-difficulty imbalance issues and thus a failure
of multilingual meta-learning ASR (MML-ASR). In this work, we solve this
problem by developing a novel adversarial meta sampling (AMS) approach to
improve MML-ASR. When sampling tasks in MML-ASR, AMS adaptively determines the
task sampling probability for each source language. Specifically, for each
source language, if the query loss is large, it means that its tasks are not
well sampled to train ASR model in terms of its quantity and difficulty and
thus should be sampled more frequently for extra learning. Inspired by this
fact, we feed the historical task query loss of all source language domain into
a network to learn a task sampling policy for adversarially increasing the
current query loss of MML-ASR. Thus, the learnt task sampling policy can master
the learning situation of each language and thus predicts good task sampling
probability for each language for more effective learning. Finally, experiment
results on two multilingual datasets show significant performance improvement
when applying our AMS on MML-ASR, and also demonstrate the applicability of AMS
to other low-resource speech tasks and transfer learning ASR approaches.Comment: accepted in AAAI202
Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
Recently deep learning based quantitative structure-activity relationship
(QSAR) models has shown surpassing performance than traditional methods for
property prediction tasks in drug discovery. However, most DL based QSAR models
are restricted to limited labeled data to achieve better performance, and also
are sensitive to model scale and hyper-parameters. In this paper, we propose
Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks.
Uni-QSAR combines molecular representation learning (MRL) of 1D sequential
tokens, 2D topology graphs, and 3D conformers with pretraining models to
leverage rich representation from large-scale unlabeled data. Without any
manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks
of the Therapeutic Data Commons (TDC) benchmark under designed parallel
workflow, with an average performance improvement of 6.09\%. Furthermore, we
demonstrate the practical usefulness of Uni-QSAR in drug discovery domains
- …