Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)—including machine learning (ML) and Big Data analytics—as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.

Computational programs accelerate the chemical discovery processes but often need proper three-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing a huge space of stereoisomers and conformers. We developed the Python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input. Auto3D is based on state-of-the-art algorithms and can automatize the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization, and ranking process. Tested on 50 molecules with multiple unspecified stereocenters, Auto3D is guaranteed to find the stereoconfiguration that yields the lowest-energy conformer. With Auto3D, we provide an extension of the ANI model. The new model, dubbed ANI-2xt, is trained on a tautomer-rich data set. ANI-2xt is benchmarked with DFT methods on geometry optimization and electronic and Gibbs free energy calculations. Compared with ANI-2x, ANI-2xt provides a 42% error reduction for tautomeric reaction energy calculations when using the gold-standard coupled-cluster calculation as the reference. ANI-2xt can accurately predict the energies and is several orders of magnitude faster than DFT methods.

There has been a recent focus within Alzheimer's Disease (AD) research on potential CSF biomarkers as diagnostic tools. PET imaging and CSF biomarkers have been used to determine the amyloid status of individuals when characterising AD. While these measures have largely correlated with each other, it is also suggested that there could be patients who can have discordant results. However, it is still unclear what proportion of AD subjects demonstrate the discordance. Additionally, whether these changes are associated with different pathological and clinical characteristics of the patients. Here, we investigated the discordance between CSF Aβ42:40 and amyloid status and evaluated whether there are any significant changes in their levels of tau aggregation. 313 participants' data were selected from the ADNI database. The cutoff for Aβ42:40 was then calculated using the Youden Index resulting from receiver operating characteristic (ROC) curve. The formula used was J = maxc Se (c) + Sp (c) - 1, resulting in a maximum cutoff of J = 0.057 for Aβ42:40. This cutoff produced a discordance rate of 10.5%, with n = 33 of 313 patients being discordant. We then performed t-tests to investigate the potential clinical differences between concordant and discordant patients. Independent samples t-tests indicated that levels of pTau181 and total Tau significantly differed between the concordant and discordant groups. pTau181 was significantly lower in the discordant group (t(71.2)=-3.166, p = .002), as was Tau (t(53.9)=-2.046, p = .046). Further data is found in Figure 1. Demonstration of different levels of tau deposition in the discordant groups implies there may be other pathological processes influencing neurodegeneration in these subjects. A discordance of 10% between CSF amyloid and PET amyloid implies that we should evaluate these subjects in greater detail before enrolling them in intervention studies. References 1. Hansson, O. et al. (2019). https://doi.org/10.1186/s13195-019-0485-0 2. Pyun, JM. et al. (2024) https://doi.org/10.1038/s41398-024-02766-6.