The Synthesizability of Molecules Proposed by Generative Models

The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early-stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. These techniques can suggest novel molecular structures intended to maximize a multi-objective function, e.g., suitability as a therapeutic against a particular target, without relying on brute-force exploration of a chemical space. However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically-tractable chemical space, although doing so necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

Molecular optimization is a fundamental goal in the chemical sciences and is of central interest to drug and material design. In recent years, significant progress has been made in solving challenging problems across various aspects of computational molecular optimizations, emphasizing high validity, diversity, and, most recently, synthesizability. Despite this progress, many papers report results on trivial or self-designed tasks, bringing additional challenges to directly assessing the performance of new methods. Moreover, the sample efficiency of the optimization--the number of molecules evaluated by the oracle--is rarely discussed, despite being an essential consideration for realistic discovery applications. To fill this gap, we have created an open-source benchmark for practical molecular optimization, PMO, to facilitate the transparent and reproducible evaluation of algorithmic advances in molecular optimization. This paper thoroughly investigates the performance of 25 molecular design algorithms on 23 tasks with a particular focus on sample efficiency. Our results show that most "state-of-the-art" methods fail to outperform their predecessors under a limited oracle budget allowing 10K queries and that no existing algorithm can efficiently solve certain molecular optimization problems in this setting. We analyze the influence of the optimization algorithm choices, molecular assembly strategies, and oracle landscapes on the optimization performance to inform future algorithm development and benchmarking. PMO provides a standardized experimental setup to comprehensively evaluate and compare new molecule optimization methods with existing ones. All code can be found at https://github.com/wenhao-gao/mol_opt

Seasonal variability does not impact in vitro fertilization success

Peer reviewedPublisher PD

Regional variation in characteristics of patients with decompensated cirrhosis admitted to hospitals in the UK

[no abstract

Upregulation of Phosphodiesterase type 5 in the Hyperplastic Prostate

Both erectile dysfunction (ED) and lower urinary tract symptoms (LUTS)/benign prostatic hyperplasia (BPH) are common in the aging male. Numerous clinical trials have demonstrated the efficacy and safety of phosphodiesterase type 5 inhibitors (PDE5-Is) for treating LUTS/BPH with/without ED. However, the influence of BPH on prostatic PDE5 expression has never been studied. A testosterone-induced rat model of BPH was developed and human hyperplastic prostate specimens were harvested during cystoprostatectomy. PDE5, nNOS, eNOS and α1-adrenoreceptor subtypes (α1aARs, α1bARs and α1dARs) were determined with real-time RT-PCR for rat tissues whilst PDE5 and α1-adrenoreceptor subtypes were determined in human samples. PDE5 was further analyzed with Western-blot and histological examination. Serum testosterone was measured with ELISA. The rat BPH model was validated as having a significantly enlarged prostate. PDE5 localized mainly in fibromuscular stroma in prostate. Our data showed a significant and previously undocumented upregulation of PDE5 in both rat and human BPH, along with increased expression of nNOS and α1d ARs for rat tissues and α1a ARs for human BPH. The upregulation of PDE5 in the hyperplastic prostate could explain the mechanism and contribute to the high effectiveness of PDE5-Is for treating LUTS/BPH. Fibromuscular stroma could be the main target for PDE5-Is within prostate

InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems

In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems. The prevailing approach is to pretrain a powerful task-agnostic model on a large unlabeled corpus but may struggle to transfer knowledge to downstream tasks. In this study, we propose InstructMol, a semi-supervised learning algorithm, to take better advantage of unlabeled examples. It introduces an instructor model to provide the confidence ratios as the measurement of pseudo-labels' reliability. These confidence scores then guide the target model to pay distinct attention to different data points, avoiding the over-reliance on labeled data and the negative influence of incorrect pseudo-annotations. Comprehensive experiments show that InstructBio substantially improves the generalization ability of molecular models, in not only molecular property predictions but also activity cliff estimations, demonstrating the superiority of the proposed method. Furthermore, our evidence indicates that InstructBio can be equipped with cutting-edge pretraining methods and used to establish large-scale and task-specific pseudo-labeled molecular datasets, which reduces the predictive errors and shortens the training process. Our work provides strong evidence that semi-supervised learning can be a promising tool to overcome the data scarcity limitation and advance molecular representation learning

Spin glass behavior in URh_2Ge_2

URh_2Ge_2 occupies an extraordinary position among the heavy-electron 122-compounds, by exhibiting a previously unidentified form of magnetic correlations at low temperatures, instead of the usual antiferromagnetism. Here we present new results of ac and dc susceptibilities, specific heat and neutron diffraction on single-crystalline as-grown URh_2Ge_2. These data clearly indicate that crystallographic disorder on a local scale produces spin glass behavior in the sample. We therefore conclude that URh_2Ge_2 is a 3D Ising-like, random-bond, heavy-fermion spin glass.Comment: 10 pages, RevTeX, with 4 postscript figures, accepted by Physical Review Letters Nov 15, 199

Tenfold Magnetoconductance in a Non-Magnetic Metal Film

We present magnetoconductance (MC) measurements of homogeneously disordered Be films whose zero field sheet conductance (G) is described by the Efros-Shklovskii hopping law $G(T)=(2e^2/h)\exp{-(T_o/T)^{1/2}}$. The low field MC of the films is negative with G decreasing 200% below 1 T. In contrast the MC above 1 T is strongly positive. At 8 T, G increases 1000% in perpendicular field and 500% in parallel field. In the simpler parallel case, we observe {\em field enhanced} variable range hopping characterized by an attenuation of $T_o$ via the Zeeman interaction.Comment: 9 pages including 5 figure

Magnetic Exciton-Polariton with Strongly Coupled Atomic and Photonic Anisotropies

Anisotropy plays a key role in science and engineering. However, the interplay between the material and engineered photonic anisotropies has hardly been explored due to the vastly different length scales. Here we demonstrate a matter-light hybrid system, exciton-polaritons in a 2D antiferromagnet, CrSBr, coupled with an anisotropic photonic crystal (PC) cavity, where the spin, atomic lattice, and photonic lattices anisotropies are strongly correlated, giving rise to unusual properties of the hybrid system and new possibilities of tuning. We show exceptionally strong coupling between engineered anisotropic optical modes and anisotropic excitons in CrSBr, which is stable against excitation densities a few orders of magnitude higher than polaritons in isotropic materials. Moreover, the polaritons feature a highly anisotropic polarization tunable by tens of degrees by controlling the matter-light coupling via, for instance, spatial alignment between the material and photonic lattices, magnetic field, temperature, cavity detuning and cavity quality-factors. The demonstrated system provides a prototype where atomic- and photonic-scale orders strongly couple, opening opportunities of photonic engineering of quantum materials and novel photonic devices, such as compact, on-chip polarized light source and polariton laser