protein engineering papers:

• The coming of age of de novo protein design (Huang, Boyken, Baker)

• Directed Evolution: Bringing New Chemistry to Life (Arnold)

• De novo design of protein structure and function with RFdiffusion (Watson et al.)

• Illuminating protein space with a programmable generative model (Ingraham et al.)

• Unsupervised evolution of protein and antibody complexes with a structure-informed language model (Shanker et al.)

• Efficient evolution of human antibodies from general protein language models (Hie et al.)

• Robust deep learning–based protein sequence design using ProteinMPNN (Dauparas et al.)

• Binding and sensing diverse small molecules using shape-complementary pseudocycles (An et al.)

• Accurate de novo design of membrane-traversing macrocycles (Bhardwaj et al.)

• Design of complicated all-α protein structures (Sakuma et al.)

• Therapeutic enzyme engineering using a generative neural network (Giessel et al.)

molecular dynamics papers:

• Atomic-Level Characterization of the Structural Dynamics of Proteins (Shaw et al.)

• Anton, a special-purpose machine for molecular dynamics simulation (Shaw et al.)

• CHARMM: The biomolecular simulation program (Brooks et al.)

• GROMACS: Fast, Flexible and Free (Van Der Spoel et al.)

protein bioinformatics papers:

• Deep learning (LeCun, Bengio, Hinton); useful for structure prediction among other applications

• Highly accurate protein structure prediction with AlphaFold (Jumper et al.)

• Highly accurate protein structure prediction for the human proteome (Tunyasuvunakool et al.)

• Accurate prediction of protein structures and interactions using a three-track neural network (Baek et al.)

• Protein Sectors: Evolutionary Units of Three-Dimensional Structure (Halabi et al.)

protein chemistry papers:

• Principles and Patterns of Protein Conformation (Jane & David Richardson)

• Stepwise protein folding at near amino acid resolution by hydrogen exchange and mass spectrometry (Hu et al.)

• The design and characterization of two proteins with 88% sequence identity but different structure and function (Alexander et al.)

• Proteins evolve on the edge of supramolecular self-assembly (Garcia-Seisdedos et al.)

• The crystal structure of the asymmetric GroEL–GroES–(ADP)7 chaperonin complex (Xu et al.)

quantitative biology papers:

• Noise in Gene Expression Determines Cell Fate in Bacillus subtilis (Maamar, Raj, and Dubnau)

• Kinetic Proofreading: A New Mechanism for Reducing Errors in Biosynthetic Processes Requiring High Specificity (Hopfield)

• Identification of common molecular subsequences (Smith and Waterman)

drug discovery reviews:

• Modulating biomolecular condensates: a novel approach to drug discovery (Mitrea et al.)

• Introduction to current and future protein therapeutics: A protein engineering perspective (Carter)

• Applications of machine learning in drug discovery and development (Vamathevan et al.)

• Role of Molecular Dynamics and Related Methods in Drug Discovery (De Vivo et al.)

articles on developing new medicines:

• Reflections on Alnylam (Maraganore)

• Try This Antibody Over Here (Lowe)

• Always the Same Warning Signs (Lowe)

• Longer Life, At A Cost (Lowe)

• The First Self-Amplifying mRNA Vaccine (Lowe)

• We Need Better Benchmarks for Machine Learning in Drug Discovery (Walters)

• AI in Drug Discovery - A Practical View From the Trenches (Walters)

videos:

• 3Blue1Brown Neural Networks (chapters 1-6)

• 3Blue1Brown Central Limit Theorem

• Kaname Naito's Japanese Teaching Videos

• Cure Dolly's video on Japanese Transitivity

• Cure Dolly's video on Older Japanese Copulas

• Cure Dolly's Video on Kanji Nuances

podcasts:

• The Long Run (Timmerman)

textbooks:

• Introduction to General, Organic, and Biochemistry (Bettelheim, Brown, Campbell, Farrell, Torres)

• Cell Biology by the Numbers (Milo, Phillips)

• Introduction to Quantum Mechanics (Griffiths, Schroeter)

• Introduction to Cosmology (Ryden)

• Modern Semiconductor Devices for Integrated Circuits (Hu)

• Operating Systems: Three Easy Pieces (Remzi & Andrea Arpaci-Dusseau, Reiher)

miscellaneous:

• Could a Neuroscientist Understand a Microprocessor? (Jonas, Kording; thanks to Joanne Peng for sending me this)

• Do not go gently into that good night: The dying brain and its paradoxically heightened electrical activity (Koch)

• The global human day (Fajzel et al.)

• Diffraction Spikes (Wikipedia)

• How Hard It Is Seeing What Is in Front of Your Eyes (Eisenberg)

• Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics (Dibot et al.)

• The Silurian hypothesis: would it be possible to detect an industrial civilization in the geological record? (Schmidt & Frank)

• Thermodynamic driving forces in contact electrification between polymeric materials (Zhang, Sundaresan, Webb; thanks to Reha Mathur for sending me this)