615.19
Drug Discovery
Targets, molecules and the path to the clinic.
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drug discovery small molecule inhibitor
machine learning drug design
615.19
Targets, molecules and the path to the clinic.
Drawer contents
Filled from
Search threads
drug discovery small molecule inhibitor
machine learning drug design
Wei Wang, Yeaji Kim, Xiaotong Zhao, Kristen Weber Bonk + 3 more
Triple-negative breast cancer (TNBC) tumors lack expression of estrogen receptor (ER), progesterone receptor (PR), and HER2, limiting the availability of targeted therapeutic options. As a result, TNBC is characterized by a high propensity for metastasis, rapid recurrence, and poor overall prognosis. Despite…
Kunyu Wang, Jon Paul Janet, Alessandro Tibo
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Priyanka Bhutada, Nitin Goyal, Tatsam K. Lakhankiya, Sai D. Narahari + 8 more
Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their utility for real-world industrial research remains insufficiently characterized. Extending the analysis presented in the first paper of this series, we evaluated the same five advanced AI…
Akankhya Mohanty, Rajesh Kumar Sahoo, A. Swaroop Sanket, Ellojita Rout
New Delhi metallo-β-lactamase-1 (NDM-1), which has emerged globally, exhibits resistance to almost all β-lactam antibiotics, including carbapenems, posing a major challenge to modern antibiotic therapy. Deep sequencing has revealed the evolution of several new NDM variants (some of the variants are more thermostable…
Patrick D. Fischer, Sebastian Hiller
“Undruggable” proteins without surface-accessible binding sites pose significant challenges to target-based drug discovery. Innovative approaches are needed to tackle these proteins. One promising strategy is targeting them in their nascent chain form at the ribosome, where they have a different conformation than in…
Zhigang Meng, Zhikai Yang, Mingxun Zhu, Jianzhen Xu
The emergence of foundation models with trillion-level parameters has redefined the landscape of artificial intelligence. Various fields are developing their own large-scale models, which can solve many problems within the field and improve work efficiency. Biological large-scale models are a cross-disciplinary…
Bin Liao, Jixiao He, Mingzhu Zhao, Xiaozhen Cui + 5 more
Deep learning has accelerated drug discovery, yet most existing models are trained using in vitro affinity datasets and consequently remain disconnected from the cellular context in which functional ligand–protein interactions occur. This limitation hinders the ability to reflect the complexity of native interactomes…
Maham Ansari, Abdul Wali, Mohhamed Rasul, Kristina Gemzell Danielsson + 2 more
Telomerase is a key regulator of both cellular immortality and aging, making it an attractive target for cancer therapy and age associated pathologies. Its clinical exploitation has been limited by structural complexity and challenges in achieving specificity. Recent structural insights into human telomerase reverse…
Srijit Seal, Akshat Shirish Zalte, David Alencar Araripe, Renan Augusto Gomes + 25 more
Machine learning (ML) models for molecular property prediction are increasingly deployed in drug discovery, yet their adoption in real-world scenarios requires an understanding of the conditions in which a model succeeds or fails. While standardized benchmarks are powerful instruments to measure and unlock progress in…
Shuanglin Qin, Rui Peng, Guangshuai Zhang, Si Yan + 7 more
Targeted protein degradation (TPD) has emerged as a transformative therapeutic strategy that offers unprecedented opportunities to eliminate traditionally “undruggable” proteins that have posed significant challenges in traditional drug development. Current TPD approaches, including proteolysis-targeting chimeras…
Zhimeng Zhou, Yang Nan, Minjie Mou, Yuntao Qian + 16 more
Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug…
Gurjit Kaur Bhatti, Anushka Verma, Komal Devi, Naina Khullar + 3 more
Nucleic Acid Therapeutics (NATs), including Antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and messenger RNAs (mRNAs), are a rapidly developing class of therapeutics capable of specifically regulating previously considered undruggable and inaccessible genes and pathways for modification by small…
Sameek Singh, Maja Wierzbińska, Konika Konika, Adam H. Libby + 7 more
Large chemoproteomic screens using covalent fragments map compound–cysteine engagements across the proteome, however, identifying robust, recognition-driven interactions remains a challenge due to experimental variability and electrophile reactivity. Here, we present CysLENS (Cysteine Ligandability Evaluation through…
Shiv Garg, Sara Garg
Quantum machine learning (QML) is widely proposed as a successor to classical models in cheminformatics, but rigorous benchmarks under realistic contemporary data constraints remain scarce. We compared two quantum classifiers (quantum support vector machine, variational quantum classifier) against gradient boosting…
Kyunghwan Yeo, Dongwoo Kim, Jaemin Sim, Juyong Lee
Ligand binding-site similarity search is a crucial step in drug discovery that reduces the conformational search space for docking and other downstream tasks by comparing a target protein against experimentally identified binding sites. Existing methods rely on either direct structural alignment or lossy compression of…
David E. Heppner, Monica R. MacDonald, Nader N. Nasief, Shilpa E. Naik + 1 more
Drug discovery scientists and chemical biologists continually seek to improve strategies for compound development. Here, we present insights derived from multiple studies of the epidermal growth factor receptor (EGFR), illustrating how this well-established target can inform practical aspects of drug discovery. Case…
Clayton W. Kosonocky, Nikol Kadeřábková, Kangsan Kim, Ayesha J.S. Mahmood + 12 more
Understanding how molecular structure encodes biological function remains a grand challenge in drug discovery. Here, we present PubCheF-1, a deep learning model that predicts literature-derived biological function directly from chemical structure. PubCheF-1 was trained on a dataset linking molecules to labels derived…
David P. Martin, Min Teng, Baskar Nammalwar, Christian Perez + 9 more
This report summarizes the discovery and optimization of a novel series of nonhydroxamate inhibitors targeting LpxC, a Zn2+-dependent hydrolase that is essential for the survival of Gram-negative bacteria. Beginning with a 5-hydroxypyrimidin-4-one metal-binding pharmacophore, structure-based approaches were utilized to…
Chia-Ho Ou, Jun-Cheng Liao, Chung-Yao Huang, Oscar K. Lee
Title: Summary Artificial intelligence (AI) has greatly expanded the generative capacity of drug discovery, yet the ability to translate large candidate pools into structured, resource-aware decisions remains limited. This review addresses this emerging optimization bottleneck by examining quantum annealing as a…