The development of data-driven approaches for solving differential equations
has been followed by a plethora of applications in science and engineering
across a multitude of disciplines and remains a central focus of active
scientific inquiry. However, a large body of natural phenomena incorporates
memory effects that are best described via fractional integro-differential
equations (FIDEs), in which the integral or differential operators accept
non-integer orders. Addressing the challenges posed by nonlinear FIDEs is a
recognized difficulty, necessitating the application of generic methods with
immediate practical relevance. This work introduces the Universal Fractional
Integro-Differential Equation Solvers (UniFIDES), a comprehensive machine
learning platform designed to expeditiously solve a variety of FIDEs in both
forward and inverse directions, without the need for ad hoc manipulation of the
equations. The effectiveness of UniFIDES is demonstrated through a collection
of integer-order and fractional problems in science and engineering. Our
results highlight UniFIDES' ability to accurately solve a wide spectrum of
integro-differential equations and offer the prospect of using machine learning
platforms universally for discovering and describing dynamical and complex
systems.

UniFIDES 是一种机器学习平台，通过解决多种正向和反向的整数阶和分数阶问题，准确求解各种整数阶和分数阶的积分微分方程，为发现和描述动态和复杂系统提供了可能性。

UniFIDES：通用分数阶积分微分方程求解器

UniFIDES: Universal Fractional Integro-Differential Equation Solvers

Virus infection is one of the major diseases that seriously threaten human
health. To meet the growing demand for mining and sharing data resources
related to antiviral drugs and to accelerate the design and discovery of new
antiviral drugs, we presented an open-access antiviral drug resource and
machine learning platform (VDDB), which, to the best of our knowledge, is the
first comprehensive dedicated resource for experimentally verified potential
drugs/molecules based on manually curated data. Currently, VDDB highlights 848
clinical vaccines, 199 clinical antibodies, as well as over 710,000 small
molecules targeting 39 medically important viruses including SARS-CoV-2.
Furthermore, VDDB stores approximately 3 million records of pharmacological
data for these collected potential antiviral drugs/molecules, involving 314
cell infection-based phenotypic and 234 target-based genotypic assays. Based on
these annotated pharmacological data, VDDB allows users to browse, search and
download reliable information about these collects for various viruses of
interest. In particular, VDDB also integrates 57 cell infection- and 117
target-based associated high-accuracy machine learning models to support
various antivirals identification-related tasks, such as compound activity
prediction, virtual screening, drug repositioning and target fishing. VDDB is
freely accessible at this http URL.

本研究提出了一个开放查询的抗病毒药物资源库和机器学习平台，拥有手工筛选的关于试验验证可能抗病毒药物分子的数据集，并支持药物活性预测、虚拟筛选和其他相关任务。